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Sample records for auto-regressive neuro-fuzzy model

  1. Neuro-fuzzy modeling in bankruptcy prediction

    Directory of Open Access Journals (Sweden)

    Vlachos D.

    2003-01-01

    Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

  2. Neuro-fuzzy system modeling based on automatic fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    Yuangang TANG; Fuchun SUN; Zengqi SUN

    2005-01-01

    A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.

  3. NEURO-FUZZY MODELLING OF BLENDING PROCESS IN CEMENT PLANT

    Directory of Open Access Journals (Sweden)

    Dauda Olarotimi Araromi

    2015-11-01

    Full Text Available The profitability of a cement plant depends largely on the efficient operation of the blending stage, therefore, there is a need to control the process at the blending stage in order to maintain the chemical composition of the raw mix near or at the desired value with minimum variance despite variation in the raw material composition. In this work, neuro-fuzzy model is developed for a dynamic behaviour of the system to predict the total carbonate content in the raw mix at different clay feed rates. The data used for parameter estimation and model validation was obtained from one of the cement plants in Nigeria. The data was pre-processed to remove outliers and filtered using smoothening technique in order to reveal its dynamic nature. Autoregressive exogenous (ARX model was developed for comparison purpose. ARX model gave high root mean square error (RMSE of 5.408 and 4.0199 for training and validation respectively. Poor fit resulting from ARX model is an indication of nonlinear nature of the process. However, both visual and statistical analyses on neuro-fuzzy (ANFIS model gave a far better result. RMSE of training and validation are 0.28167 and 0.7436 respectively, and the sum of square error (SSE and R-square are 39.6692 and 0.9969 respectively. All these are an indication of good performance of ANFIS model. This model can be used for control design of the process.

  4. Adaptive neuro fuzzy inference system modeling to predict damage level of non-reshaped berm breakwater

    Digital Repository Service at National Institute of Oceanography (India)

    Harish, N.; Mandal, S.; Rao, S.; Lokesha

    The Adaptive Neuro Fuzzy Inference System (ANFIS) model is constructed using experimental data set to predict the damage level of berm breakwater. Experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory...

  5. CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Hansen, Lars Kai

    2004-01-01

    We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least squares...

  6. Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration

    Science.gov (United States)

    Kottapalli, Sesi; Malki, Heidar A.; Langari, Reza

    2003-01-01

    Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/Army Airloads Program flight test database was the source of the 200 point database. The present study is conducted in two parts. The first part involves level flight conditions and the second part involves the entire (200 point) database including maneuver conditions. The results show that a neuro-fuzzy model can successfully predict the pilot vibration. Also, it is found that the training phase of this neuro-fuzzy model takes only two or three iterations to converge for most cases. Thus, the proposed approach produces a potentially viable model for real-time implementation.

  7. Modeling of a HTPEM fuel cell using Adaptive Neuro-Fuzzy Inference Systems

    DEFF Research Database (Denmark)

    Justesen, Kristian Kjær; Andreasen, Søren Juhl; Sahlin, Simon Lennart

    2015-01-01

    In this work an Adaptive Neuro-Fuzzy Inference System (ANFIS) model of the voltage of a fuel cell is developed. The inputs of this model are the fuel cell temperature, current density and the carbon monoxide concentration of the anode supply gas. First an identification experiment which spans...

  8. Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks

    Institute of Scientific and Technical Information of China (English)

    Jie Zhang

    2006-01-01

    In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a "global" nonlinear long-range prediction model through the fuzzy conjunction of a number of "local" linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.

  9. Short-Term Electrical Load Forecasting using Neuro-Fuzzy Models

    Energy Technology Data Exchange (ETDEWEB)

    Park, Young Jin; Shim, Hyun Jeong; Wang, Bo Hyeun [Kang Nung National University (Korea)

    2000-03-01

    This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models, The primary goal of the proposed method is to improve the performance of the prediction model in terms of accuracy and reliability. For this, the proposed method explores the advantages of the structure learning of the neuro-fuzzy model. The proposed load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized model. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1993 and 1994 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability compared with the prediction systems based on multilayer perceptions, radial basis function networks, and neuro-fuzzy models without the structure learning. (author). 23 refs., 11 figs., 8 tabs.

  10. Adaptive Neuro-Fuzzy Inference System Models for Force Prediction of a Mechatronic Flexible Structure

    DEFF Research Database (Denmark)

    Achiche, S.; Shlechtingen, M.; Raison, M.

    2016-01-01

    This paper presents the results obtained from a research work investigating the performance of different Adaptive Neuro-Fuzzy Inference System (ANFIS) models developed to predict excitation forces on a dynamically loaded flexible structure. For this purpose, a flexible structure is equipped with ...

  11. Adaptive neuro-fuzzy modeling of transient heat transfer in circular duct air flow

    Energy Technology Data Exchange (ETDEWEB)

    Hasiloglu, Abdulsamet [Department of Electronics and Telecommunications Engineering, Engineering Faculty, Ataturk University, Erzurum (Turkey); Yilmaz, Mehmet; Comakli, Omer [Department of Mechanical Engineering, Engineering Faculty, Ataturk University, Erzurum (Turkey); Ekmekci, Ismail [Department of Mechanical Engineering, Engineering Faculty, Sakarya University, Sakarya (Turkey)

    2004-11-01

    The aim of this study is to demonstrate the usefulness of an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of transient heat transfer. An ANFIS has been applied for the transient heat transfer in thermally and simultaneously developing circular duct flow, subjected to a sinusoidally varying inlet temperature. The experiments covered Reynolds numbers in the 2528{<=}Re{<=}4265 range and inlet heat input in the 0.01{<=}{beta}{<=}0.96 Hz frequency range. The accuracy of predictions and the adaptability of the ANFIS were examined, and good predictions were achieved for the temperature amplitudes of the transient heat transfer in thermally and simultaneously developing circular duct flow. The results show that the neuro-fuzzy can be used for modeling transient heat transfer in ducts. The results obtained with the ANFIS are also compared to those of a multiple linear regression and a neural network with a multi-layered feed-forward back-propagation algorithm. (authors)

  12. A Neuro-Fuzzy Approach for Modelling Electricity Demand in Victoria

    OpenAIRE

    Abraham, Ajith; Nath, Baikunth

    2004-01-01

    Neuro-fuzzy systems have attracted growing interest of researchers in various scientific and engineering areas due to the increasing need of intelligent systems. This paper evaluates the use of two popular soft computing techniques and conventional statistical approach based on Box--Jenkins autoregressive integrated moving average (ARIMA) model to predict electricity demand in the State of Victoria, Australia. The soft computing methods considered are an evolving fuzzy neural network (EFuNN) ...

  13. Adaptive Neuro-Fuzzy Modeling of Mechanical Behavior for Vertically Aligned Carbon Nanotube Turfs

    Institute of Scientific and Technical Information of China (English)

    Mohammad A1-Khedher; Charles Pezeshki; Jeanne McHale; GFritz Knorr

    2011-01-01

    Several characterization methods have been developed to investigate the mechanical and structural properties of vertically aligned carbon nanotubes (VACNTs). Establishing analytical models at nanoscale to interpret these properties is complicated due to the nonuniformity and irregularity in quality of as-grown samples.In this paper, we propose a new methodology to investigate the correlation between indentation resistance of multi-wall carbon nanotube (MWCNT) turfs, Raman spectra and the geometrical properties of the turf structure using adaptive neuro-fuzzy phenomenological modeling. This methodology yields a novel approach for modeling at the nanoscale by evaluating the effect of structural morphologies on nanomaterial properties using Raman spectroscopy.

  14. Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications

    Directory of Open Access Journals (Sweden)

    A.A. Fahmy

    2013-12-01

    Full Text Available This paper presents a new neuro-fuzzy controller for robot manipulators. First, an inductive learning technique is applied to generate the required inverse modeling rules from input/output data recorded in the off-line structure learning phase. Second, a fully differentiable fuzzy neural network is developed to construct the inverse dynamics part of the controller for the online parameter learning phase. Finally, a fuzzy-PID-like incremental controller was employed as Feedback servo controller. The proposed control system was tested using dynamic model of a six-axis industrial robot. The control system showed good results compared to the conventional PID individual joint controller.

  15. Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model.

    Directory of Open Access Journals (Sweden)

    Georgina Cosma

    Full Text Available The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA level, the biopsy most common tumor pattern (Primary Gleason pattern and the second most common tumor pattern (Secondary Gleason pattern in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD or Extra-Prostatic Disease (ED using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC, with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812. The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR

  16. Neuro-fuzzy models for systems identification applied to the operation of nuclear power plants; Sistemas neuro-fuzzy para identificacao de sistemas aplicados a operacao de centrais nucleares

    Energy Technology Data Exchange (ETDEWEB)

    Alves, Antonio Carlos Pinto Dias

    2000-09-01

    A nuclear power plant has a myriad of complex system and sub-systems that, working cooperatively, make the control of the whole plant. Nevertheless their operation be automatic most of the time, the integral understanding of their internal- logic can be away of the comprehension of even experienced operators because of the poor interpretability those controls offer. This difficulty does not happens only in nuclear power plants but in almost every a little more complex control system. Neuro-fuzzy models have been used for the last years in a attempt of suppress these difficulties because of their ability of modelling in linguist form even a system which behavior is extremely complex. This is a very intuitive human form of interpretation and neuro-fuzzy model are gathering increasing acceptance. Unfortunately, neuro-fuzzy models can grow up to become of hard interpretation because of the complexity of the systems under modelling. In general, that growing occurs in function of redundant rules or rules that cover a very little domain of the problem. This work presents an identification method for neuro-fuzzy models that not only allows models grow in function of the existent complexity but that beforehand they try to self-adapt to avoid the inclusion of new rules. This form of construction allowed to arrive to highly interpretative neuro-fuzzy models even of very complex systems. The use of this kind of technique in modelling the control of the pressurizer of a PWR nuclear power plant allowed verify its validity and how neuro-fuzzy models so built can be useful in understanding the automatic operation of a nuclear power plant. (author)

  17. Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

    Science.gov (United States)

    Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

    2012-06-01

    SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

  18. An Electromyographic-driven Musculoskeletal Torque Model using Neuro-Fuzzy System Identification: A Case Study.

    Science.gov (United States)

    Jafari, Zohreh; Edrisi, Mehdi; Marateb, Hamid Reza

    2014-10-01

    The purpose of this study was to estimate the torque from high-density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate-to-high isometric elbow flexion-extension. The elbow torque was estimated in two following steps: First, surface electromyography (EMG) amplitudes were estimated using principal component analysis, and then a fuzzy model was proposed to illustrate the relationship between the EMG amplitudes and the measured torque signal. A neuro-fuzzy method, with which the optimum number of rules could be estimated, was used to identify the model with suitable complexity. Utilizing the proposed neuro-fuzzy model, the clinical interpretability was introduced; contrary to the previous linear and nonlinear black-box system identification models. It also reduced the estimation error compared with that of the most recent and accurate nonlinear dynamic model introduced in the literature. The optimum number of the rules for all trials was 4 ± 1, that might be related to motor control strategies and the % variance accounted for criterion was 96.40 ± 3.38 which in fact showed considerable improvement compared with the previous methods. The proposed method is thus a promising new tool for EMG-Torque modeling in clinical applications.

  19. Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing Gene Regulatory Networks

    Institute of Scientific and Technical Information of China (English)

    Guixia Liu; Lei Liu; Chunyu Liu; Ming Zheng; Lanying Su; Chunguang Zhou

    2011-01-01

    Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly, in this paper, we propose a novel approach based on combining neuro-fuzzy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory networks, but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without factitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast. The results show that this approach can work effectively.

  20. Gas composition modeling in a reformed Methanol Fuel Cell system using adaptive Neuro-Fuzzy Inference Systems

    DEFF Research Database (Denmark)

    Justesen, Kristian Kjær; Andreasen, Søren Juhl; Shaker, Hamid Reza

    2013-01-01

    This work presents a method for modeling the gas composition in a Reformed Methanol Fuel Cell system. The method is based on Adaptive Neuro-Fuzzy-Inference-Systems which are trained on experimental data. The developed models are of the H2, CO2, CO and CH3OH mass flows of the reformed gas. The ANFIS......, or fuel cell diagnostics systems....

  1. Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

    Science.gov (United States)

    Gani, Abdullah; Mohammadi, Kasra; Shamshirband, Shahaboddin; Khorasanizadeh, Hossein; Seyed Danesh, Amir; Piri, Jamshid; Ismail, Zuraini; Zamani, Mazdak

    2016-08-01

    The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neuro-fuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44-0.61 kWh/m2, 0.50-0.71 kWh/m2, and 0.78-0.91, respectively.

  2. Nonlinear Modeling and Neuro-Fuzzy Control of PEMFC

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The proton exchange membrane generation technology is highly efficient, and clean and is considered as the most hopeful "green" power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermodynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model and control online.This paper analyzed the characters of the PEMFC; and used the approach and self-study ability of artificial neural networks to build the model of nonlinear system, and adopted the adaptive neural-networks fuzzy infer system to build the temperature model of PEMFC which is used as the reference model of the control system, and adjusted the model parameters to control online. The model and control were implemented in SIMULINK environment.The results of simulation show the test data and model have a good agreement. The model is useful for the optimal and real time control of PEMFC system.

  3. Simulation of neuro-fuzzy model for optimization of combine header setting

    Directory of Open Access Journals (Sweden)

    S Zareei

    2016-09-01

    Full Text Available Introduction The noticeable proportion of producing wheat losses occur during production and consumption steps and the loss due to harvesting with combine harvester is regarded as one of the main factors. A grain combines harvester consists of different sets of equipment and one of the most important parts is the header which comprises more than 50% of the entire harvesting losses. Some researchers have presented regression equation to estimate grain loss of combine harvester. The results of their study indicated that grain moisture content, reel index, cutter bar speed, service life of cutter bar, tine spacing, tine clearance over cutter bar, stem length were the major parameters affecting the losses. On the other hand, there are several researchswhich have used the variety of artificial intelligence methods in the different aspects of combine harvester. In neuro-fuzzy control systems, membership functions and if-then rules were defined through neural networks. Sugeno- type fuzzy inference model was applied to generate fuzzy rules from a given input-output data set due to its less time-consuming and mathematically tractable defuzzification operation for sample data-based fuzzy modeling. In this study, neuro-fuzzy model was applied to develop forecasting models which can predict the combine header loss for each set of the header parameter adjustments related to site-specific information and therefore can minimize the header loss. Materials and Methods The field experiment was conducted during the harvesting season of 2011 at the research station of the Faulty of Agriculture, Shiraz University, Shiraz, Iran. The wheat field (CV. Shiraz was harvested with a Claas Lexion-510 combine harvester. The factors which were selected as main factors influenced the header performance were three levels of reel index (RI (forward speed of combine harvester divided by peripheral speed of reel (1, 1.2, 1.5, three levels of cutting height (CH(25, 30, 35 cm, three

  4. Linking Simple Economic Theory Models and the Cointegrated Vector AutoRegressive Model

    DEFF Research Database (Denmark)

    Møller, Niels Framroze

    This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its...

  5. Condition monitoring with wind turbine SCADA data using Neuro-Fuzzy normal behavior models

    DEFF Research Database (Denmark)

    Schlechtingen, Meik; Santos, Ilmar

    2012-01-01

    cause is given. In case of fault patterns earlier unseen the generic rules allow general statements about the signal behavior which highlight the anomaly. Within the current research project this method is applied to 18 onshore turbines of the 2 MW class operating since April 2009. First results show......This paper presents the latest research results of a project that focuses on normal behavior models for condition monitoring of wind turbines and their components, via ordinary Supervisory Control And Data Acquisition (SCADA) data. In this machine learning approach Adaptive Neuro-Fuzzy Interference...... of the prediction error is used as an indicator for normal and abnormal behavior, with respect to the learned behavior. The advantage of this approach is that the prediction error is widely decoupled from the typical fluctuations of the SCADA data caused by the different turbine operational modes. To classify...

  6. Analysis, Interpretation, and Recognition of Facial Action Units and Expressions Using Neuro-Fuzzy Modeling

    CERN Document Server

    Khademi, Mahmoud; Manzuri-Shalmani, Mohammad T; Kiaei, Ali A

    2010-01-01

    In this paper an accurate real-time sequence-based system for representation, recognition, interpretation, and analysis of the facial action units (AUs) and expressions is presented. Our system has the following characteristics: 1) employing adaptive-network-based fuzzy inference systems (ANFIS) and temporal information, we developed a classification scheme based on neuro-fuzzy modeling of the AU intensity, which is robust to intensity variations, 2) using both geometric and appearance-based features, and applying efficient dimension reduction techniques, our system is robust to illumination changes and it can represent the subtle changes as well as temporal information involved in formation of the facial expressions, and 3) by continuous values of intensity and employing top-down hierarchical rule-based classifiers, we can develop accurate human-interpretable AU-to-expression converters. Extensive experiments on Cohn-Kanade database show the superiority of the proposed method, in comparison with support vect...

  7. Blind identification of threshold auto-regressive model for machine fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    LI Zhinong; HE Yongyong; CHU Fulei; WU Zhaotong

    2007-01-01

    A blind identification method was developed for the threshold auto-regressive (TAR) model. The method had good identification accuracy and rapid convergence, especially for higher order systems. The proposed method was then combined with the hidden Markov model (HMM) to determine the auto-regressive (AR) coefficients for each interval used for feature extraction, with the HMM as a classifier. The fault diagnoses during the speed-up and speed- down processes for rotating machinery have been success- fully completed. The result of the experiment shows that the proposed method is practical and effective.

  8. A Study of Wind Statistics Through Auto-Regressive and Moving-Average (ARMA) Modeling

    Institute of Scientific and Technical Information of China (English)

    尹彰; 周宗仁

    2001-01-01

    Statistical properties of winds near the Taichung Harbour are investigated. The 26 years′incomplete data of wind speeds, measured on an hourly basis, are used as reference. The possibility of imputation using simulated results of the Auto-Regressive (AR), Moving-Average (MA), and/or Auto-Regressive and Moving-Average (ARMA) models is studied. Predictions of the 25-year extreme wind speeds based upon the augmented data are compared with the original series. Based upon the results, predictions of the 50- and 100-year extreme wind speeds are then made.

  9. Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

    Institute of Scientific and Technical Information of China (English)

    Hasan ABBASI NOZARI; Hamed DEHGHAN BANADAKI; Mohammad MOKHTARE; Somaveh HEKMATI VAHED

    2012-01-01

    This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system.A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT),which is an incremental tree-based learning algorithm.The proposed NF models are compared with other known intelligent identifiers,namely multilayer perceptron (MLP) and radial basis function (RBF).Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system.Experimental results show the effectiveness of our proposed NF modelling approach.

  10. Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques

    Science.gov (United States)

    Kisi, Ozgur; Sanikhani, Hadi; Cobaner, Murat

    2016-05-01

    The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models' accuracy was also investigated. Including periodicity component in models' inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.

  11. Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge.

    Science.gov (United States)

    Cakmakci, Mehmet

    2007-09-01

    Modelling of anaerobic digestion systems is difficult because their performance is complex and varies significantly with influent characteristics and operational conditions. In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) were used for modelling of anaerobic digestion system of primary sludge of Kayseri municipal WasteWater Treatment Plant (WWTP). Effluent Volatile Solid (VS) and methane yield were predicted by the ANFIS. Two stage models were performed. In the first stage, effluent VS concentration was predicted using pH, VS concentration, flowrate of pre-thickened sludge and temperature of the influent as input parameters. In the second stage, effluent VS concentration in addition to first stage input parameters were used as input parameters to predict methane yield. The low Root Mean Square Error (RMSE) and high Index of agreement (IA) values were obtained with subtractive clustering method of a first order Sugeno type inference. The model performance was evaluated with statistical parameters. According to statistical evaluations, the models satisfactorily predict effluent VS concentration and methane yield.

  12. Optimization of alkali catalyst for transesterification of jatropha curcus using adaptive neuro-fuzzy modeling

    Directory of Open Access Journals (Sweden)

    Vipan K Sohpal

    2014-06-01

    Full Text Available Transesterification of Jatropha curcus for biodiesel production is a kinetic control process, which is complex in nature and controlled by temperature, the molar ratio, mixing intensity and catalyst process parameters. A precise choice of catalyst is required to improve the rate of transesterification and to simulate the kinetic study in a batch reactor. The present paper uses an Adaptive Neuro-Fuzzy Inference System (ANFIS approach to model and simulate the butyl ester production using alkaline catalyst (NaOH. The amounts of catalyst and time for reaction have been used as the model’s input parameters. The model is a combination of fuzzy inference and artificial neural network, including a set of fuzzy rules which have been developed directly from experimental data. The proposed modeling approach has been verified by comparing the expected results with the practical results which were observed and obtained through a batch reactor operation. The application of the ANFIS test shows which amount of catalyst predicted by the proposed model is suitable and in compliance with the experimental values at 0.5% level of significance.

  13. Time-varying parameter auto-regressive models for autocovariance nonstationary time series

    Institute of Scientific and Technical Information of China (English)

    FEI WanChun; BAI Lun

    2009-01-01

    In this paper,autocovariance nonstationary time series is clearly defined on a family of time series.We propose three types of TVPAR (time-varying parameter auto-regressive) models:the full order TVPAR model,the time-unvarying order TVPAR model and the time-varying order TVPAR model for autocovariance nonstationary time series.Related minimum AIC (Akaike information criterion) estimations are carried out.

  14. Time-varying parameter auto-regressive models for autocovariance nonstationary time series

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    In this paper, autocovariance nonstationary time series is clearly defined on a family of time series. We propose three types of TVPAR (time-varying parameter auto-regressive) models: the full order TVPAR model, the time-unvarying order TVPAR model and the time-varying order TV-PAR model for autocovariance nonstationary time series. Related minimum AIC (Akaike information criterion) estimations are carried out.

  15. Predicting heartbeat arrival time for failure detection over internet using auto-regressive exogenous model

    Institute of Scientific and Technical Information of China (English)

    Zhao Haijun; Ma Yan; Huang Xiaohong; Su Yujie

    2008-01-01

    Predicting heartbeat message arrival time is crucial for the quality of failure detection service over internet. However, internet dynamic characteristics make it very difficult to understand message behavior and accurately predict heartbeat arrival time. To solve this problem, a novel black-box model is proposed to predict the next heartbeat arrival time. Heartbeat arrival time is modeled as auto-regressive process, heartbeat sending time is modeled as exogenous variable, the model's coefficients are estimated based on the sliding window of observations and this result is used to predict the next heartbeat arrival time. Simulation shows that this adaptive auto-regressive exogenous (ARX) model can accurately capture heartbeat arrival dynamics and minimize prediction error in different network environments.

  16. An adaptive neuro fuzzy model for estimating the reliability of component-based software systems

    Directory of Open Access Journals (Sweden)

    Kirti Tyagi

    2014-01-01

    Full Text Available Although many algorithms and techniques have been developed for estimating the reliability of component-based software systems (CBSSs, much more research is needed. Accurate estimation of the reliability of a CBSS is difficult because it depends on two factors: component reliability and glue code reliability. Moreover, reliability is a real-world phenomenon with many associated real-time problems. Soft computing techniques can help to solve problems whose solutions are uncertain or unpredictable. A number of soft computing approaches for estimating CBSS reliability have been proposed. These techniques learn from the past and capture existing patterns in data. The two basic elements of soft computing are neural networks and fuzzy logic. In this paper, we propose a model for estimating CBSS reliability, known as an adaptive neuro fuzzy inference system (ANFIS, that is based on these two basic elements of soft computing, and we compare its performance with that of a plain FIS (fuzzy inference system for different data sets.

  17. Probing turbulence intermittency via Auto-Regressive Moving-Average models

    CERN Document Server

    Faranda, Davide; Dubrulle, Berengere; Daviaud, Francois

    2014-01-01

    We suggest a new approach to probing intermittency corrections to the Kolmogorov law in turbulent flows based on the Auto-Regressive Moving-Average modeling of turbulent time series. We introduce a new index $\\Upsilon$ that measures the distance from a Kolmogorov-Obukhov model in the Auto-Regressive Moving-Average models space. Applying our analysis to Particle Image Velocimetry and Laser Doppler Velocimetry measurements in a von K\\'arm\\'an swirling flow, we show that $\\Upsilon$ is proportional to the traditional intermittency correction computed from the structure function. Therefore it provides the same information, using much shorter time series. We conclude that $\\Upsilon$ is a suitable index to reconstruct the spatial intermittency of the dissipation in both numerical and experimental turbulent fields.

  18. Linking Simple Economic Theory Models and the Cointegrated Vector AutoRegressive Model

    DEFF Research Database (Denmark)

    Møller, Niels Framroze

    This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its stru....... Further fundamental extensions and advances to more sophisticated theory models, such as those related to dynamics and expectations (in the structural relations) are left for future papers......This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its......, it is demonstrated how other controversial hypotheses such as Rational Expectations can be formulated directly as restrictions on the CVAR-parameters. A simple example of a "Neoclassical synthetic" AS-AD model is also formulated. Finally, the partial- general equilibrium distinction is related to the CVAR as well...

  19. Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

    Directory of Open Access Journals (Sweden)

    Hong-Juan Li

    2013-04-01

    Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

  20. NEURO FUZZY LINK BASED CLASSIFIER FOR THE ANALYSIS OF BEHAVIOR MODELS IN SOCIAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Indira Priya Ponnuvel

    2014-01-01

    Full Text Available In this study, a new link based classifier using neuro fuzzy logic has been proposed for analyzing the social behavior based on Weblog dataset. In this system, data are processed using a multistage structure. This system provides a diagnosis using a neuro fuzzy link based classifier that analyses the user’s behavior to specific diagnostic categories based on their cluster category in social networks. It uses random walks method to organize the labels. Since the links present in the social network graph frequently represent relationships among the users with respect to social contacts and behaviours, this work observes the links of the graph in order to identify the relationships represented in the graph between the users of the social network based on some new social network metrics and the past behaviour of the users. This work is useful to provide connection between consolidated features of users based on network data and also using the traditional metrics used in the analysis of social network users. From the experiments conducted in this research work, it is observed that the proposed work provides better classification accuracy due to the application of neuro fuzzy classification method in link analysis.

  1. A self-organizing power system stabilizer using Fuzzy Auto-Regressive Moving Average (FARMA) model

    Energy Technology Data Exchange (ETDEWEB)

    Park, Y.M.; Moon, U.C. [Seoul National Univ. (Korea, Republic of). Electrical Engineering Dept.; Lee, K.Y. [Pennsylvania State Univ., University Park, PA (United States). Electrical Engineering Dept.

    1996-06-01

    This paper presents a self-organizing power system stabilizer (SOPSS) which use the Fuzzy Auto-Regressive Moving Average (FARMA) model. The control rules and the membership functions of the proposed logic controller are generated automatically without using any plant model. The generated rules are stored in the fuzzy rule space and updated on-line by a self-organizing procedure. To show the effectiveness of the proposed controller, comparison with a conventional controller for one-machine infinite-bus system is presented.

  2. A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data

    Science.gov (United States)

    Ashrafi, Mohammad; Chua, Lloyd Hock Chye; Quek, Chai; Qin, Xiaosheng

    2017-02-01

    Current state-of-the-art online neuro fuzzy models (NFMs) such as DENFIS (Dynamic Evolving Neural-Fuzzy Inference System) have been used for runoff forecasting. Online NFMs adopt a local learning approach and are able to adapt to changes continuously. The DENFIS model however requires upper/lower bound for normalization and also the number of rules increases monotonically. This requirement makes the model unsuitable for use in basins with limited data, since a priori data is required. In order to address this and other drawbacks of current online models, the Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) is adopted in this study for forecast applications in basins with limited data. GSETSK is a fully-online NFM which updates its structure and parameters based on the most recent data. The model does not require the need for historical data and adopts clustering and rule pruning techniques to generate a compact and up-to-date rule-base. GSETSK was used in two forecast applications, rainfall-runoff (a catchment in Sweden) and river routing (Lower Mekong River) forecasts. Each of these two applications was studied under two scenarios: (i) there is no prior data, and (ii) only limited data is available (1 year for the Swedish catchment and 1 season for the Mekong River). For the Swedish Basin, GSETSK model results were compared to available results from a calibrated HBV (Hydrologiska Byråns Vattenbalansavdelning) model. For the Mekong River, GSETSK results were compared against the URBS (Unified River Basin Simulator) model. Both comparisons showed that results from GSETSK are comparable with the physically based models, which were calibrated with historical data. Thus, even though GSETSK was trained with a very limited dataset in comparison with HBV or URBS, similar results were achieved. Similarly, further comparisons between GSETSK with DENFIS and the RBF (Radial Basis Function) models highlighted further advantages of GSETSK as having a rule-base (compared to

  3. Auto-Regressive Models of Non-Stationary Time Series with Finite Length

    Institute of Scientific and Technical Information of China (English)

    FEI Wanchun; BAI Lun

    2005-01-01

    To analyze and simulate non-stationary time series with finite length, the statistical characteristics and auto-regressive (AR) models of non-stationary time series with finite length are discussed and studied. A new AR model called the time varying parameter AR model is proposed for solution of non-stationary time series with finite length. The auto-covariances of time series simulated by means of several AR models are analyzed. The result shows that the new AR model can be used to simulate and generate a new time series with the auto-covariance same as the original time series. The size curves of cocoon filaments regarded as non-stationary time series with finite length are experimentally simulated. The simulation results are significantly better than those obtained so far, and illustrate the availability of the time varying parameter AR model. The results are useful for analyzing and simulating non-stationary time series with finite length.

  4. NEURO FUZZY MODEL FOR FACE RECOGNITION WITH CURVELET BASED FEATURE IMAGE

    Directory of Open Access Journals (Sweden)

    SHREEJA R,

    2011-06-01

    Full Text Available A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other biometric techniques such as fingerprint or iris recognition systems. Every face has approximately 80 nodal points like (Distance between the eyes, Width of the nose etc.The basic face recognition system capture the sample, extract feature, compare template and perform matching. In this paper two methods of face recognition are compared- neural networks and neuro fuzzy method. For this curvelet transform is used for feature extraction. Feature vector is formed by extracting statistical quantities of curve coefficients. From the statistical results it is concluded that neuro fuzzy method is the better technique for face recognition as compared to neural network.

  5. Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro-fuzzy inference system (ANFIS)

    Institute of Scientific and Technical Information of China (English)

    Karami Alireza; Afiuni-Zadeh Somaieh

    2013-01-01

    One of the most important characters of blasting, a basic step of surface mining, is rock fragmentation because it directly effects on the costs of drilling and economics of the subsequent operations of loading, hauling and crushing in mines. Adaptive neuro-fuzzy inference system (ANFIS) and radial basis function (RBF) show potentials for modeling the behavior of complex nonlinear processes such as those involved in fragmentation due to blasting of rocks. We developed ANFIS and RBF methods for modeling of sizing of rock fragmentation due to bench blasting by estimation of 80%passing size (K80) of Golgohar iron mine of Sirjan, Iran. Comparing the results of ANFIS and RBF models shows that although the statistical parame-ters RBF model is acceptable but ANFIS proposed model is superior and also simpler because ANFIS model is constructed using only two input parameters while seven input parameters used for construction of RBF model.

  6. Efficient Blind System Identification of Non-Gaussian Auto-Regressive Models with HMM Modeling of the Excitation

    DEFF Research Database (Denmark)

    Li, Chunjian; Andersen, Søren Vang

    2007-01-01

    We propose two blind system identification methods that exploit the underlying dynamics of non-Gaussian signals. The two signal models to be identified are: an Auto-Regressive (AR) model driven by a discrete-state Hidden Markov process, and the same model whose output is perturbed by white Gaussian...

  7. A Vector Auto Regression Model Applied to Real Estate Development Investment: A Statistic Analysis

    Directory of Open Access Journals (Sweden)

    Fengyun Liu

    2016-10-01

    Full Text Available This study analyzes the economic system dynamics of investment in real estate from mainly four participants in China. Local governments limit the supply of commercial and residential land to raise fiscal revenue, and expand debts by land mortgage to develop industrial zones and parks. Led by local government, banks and real estate development enterprises forge a coalition on real estate investment and facilitate real estate price appreciation. The above theoretical model is empirically evidenced with VAR (Vector Auto Regression methodology. A panel VAR model shows that land leasing and real estate price appreciation positively affect local government general fiscal revenue. Additional VAR models find that bank credit in addition to private and foreign funds respectively have strong positive dynamic effects on housing prices. Housing prices also have a strong positive impact on speculation from private funds and hot money.

  8. Inverse neuro-fuzzy MR damper model and its application in vibration control of vehicle suspension system

    Science.gov (United States)

    Zong, Lu-Hang; Gong, Xing-Long; Guo, Chao-Yang; Xuan, Shou-Hu

    2012-07-01

    In this paper, a magneto-rheological (MR) damper-based semi-active controller for vehicle suspension is developed. This system consists of a linear quadratic Gauss (LQG) controller as the system controller and an adaptive neuro-fuzzy inference system (ANFIS) inverse model as the damper controller. First, a modified Bouc-Wen model is proposed to characterise the forward dynamic characteristics of the MR damper based on the experimental data. Then, an inverse MR damper model is built using ANFIS technique to determine the input current so as to gain the desired damping force. Finally, a quarter-car suspension model together with the MR damper is set up, and a semi-active controller composed of the LQG controller and the ANFIS inverse model is designed. Simulation results demonstrate that the desired force can be accurately tracked using the ANFIS technique and the semi-active controller can achieve competitive performance as that of active suspension.

  9. A comparative study for the concrete compressive strength estimation using neural network and neuro-fuzzy modelling approaches

    Science.gov (United States)

    Bilgehan, Mahmut

    2011-03-01

    In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) model have been successfully used for the evaluation of relationships between concrete compressive strength and ultrasonic pulse velocity (UPV) values using the experimental data obtained from many cores taken from different reinforced concrete structures having different ages and unknown ratios of concrete mixtures. A comparative study is made using the neural nets and neuro-fuzzy (NF) techniques. Statistic measures were used to evaluate the performance of the models. Comparing of the results, it is found that the proposed ANFIS architecture with Gaussian membership function is found to perform better than the multilayer feed-forward ANN learning by backpropagation algorithm. The final results show that especially the ANFIS modelling may constitute an efficient tool for prediction of the concrete compressive strength. Architectures of the ANFIS and neural network established in the current study perform sufficiently in the estimation of concrete compressive strength, and particularly ANFIS model estimates closely follow the desired values. Both ANFIS and ANN techniques can be used in conditions where too many structures are to be examined in a restricted time. The presented approaches enable to practically find concrete strengths in the existing reinforced concrete structures, whose records of concrete mixture ratios are not available or present. Thus, researchers can easily evaluate the compressive strength of concrete specimens using UPV and density values. These methods also contribute to a remarkable reduction in the computational time without any significant loss of accuracy. A comparison of the results clearly shows that particularly the NF approach can be used effectively to predict the compressive strength of concrete using UPV and density values. In addition, these model architectures can be used as a nondestructive procedure for health monitoring of

  10. A Genetic-Neuro-Fuzzy inferential model for diagnosis of tuberculosis

    Directory of Open Access Journals (Sweden)

    Mumini Olatunji Omisore

    2017-01-01

    Full Text Available Tuberculosis is a social, re-emerging infectious disease with medical implications throughout the globe. Despite efforts, the coverage of tuberculosis disease (with HIV prevalence in Nigeria rose from 2.2% in 1991 to 22% in 2013 and the orthodox diagnosis methods available for Tuberculosis diagnosis were been faced with a number of challenges which can, if measure not taken, increase the spread rate; hence, there is a need for aid in diagnosis of the disease. This study proposes a technique for intelligent diagnosis of TB using Genetic-Neuro-Fuzzy Inferential method to provide a decision support platform that can assist medical practitioners in administering accurate, timely, and cost effective diagnosis of Tuberculosis. Performance evaluation observed, using a case study of 10 patients from St. Francis Catholic Hospital Okpara-In-Land (Delta State, Nigeria, shows sensitivity and accuracy results of 60% and 70% respectively which are within the acceptable range of predefined by domain experts.

  11. Neuro-Fuzzy Phasing of Segmented Mirrors

    Science.gov (United States)

    Olivier, Philip D.

    1999-01-01

    A new phasing algorithm for segmented mirrors based on neuro-fuzzy techniques is described. A unique feature of this algorithm is the introduction of an observer bank. Its effectiveness is tested in a very simple model with remarkable success. The new algorithm requires much less computational effort than existing algorithms and therefore promises to be quite useful when implemented on more complex models.

  12. Designing a Battlefield Fire Support System Using Adaptive Neuro-Fuzzy Inference System Based Model

    Directory of Open Access Journals (Sweden)

    Kerim Goztepe

    2014-07-01

    Full Text Available Fire support of the maneuver operation is a continuous process. It begins with the receiving the task by the maneuver commander and continues until the mission is completed. Yet it is a key issue in combat in the way gain success. Therefore, a real-time mannered solution to fire support problem is a vital component of tactical warfare to the sequence that auxiliary forces or logistic support arrives at the theatre. A new method for deciding on combat fire support is proposed using adaptive neuro-fuzzy inference system (ANFIS in this paper. This study addresses the design of an ANFIS as an efficient tool for real-time decision-making in order to produce the best fire support plan in battlefield. Initially, criteria that are determined for the problem are formed by applying ANFIS method. Then, the ANFIS structure is built up by using the data related to selected criteria. The proposed method is illustrated by a sample fire support planning in combat. Results showed us that ANFIS is valid especially for small unit fire support planning and is useful to decrease the decision time in battlefield.

  13. Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro-fuzzy inference system and radial basis function

    Institute of Scientific and Technical Information of China (English)

    Karami Alireza; Afiuni-Zadeh Somaieh

    2012-01-01

    One of the most important characters of blasting,a basic step of surface mining,is rock fragmentation.It directly effects on the costs of drilling and economics of the subsequent operations of loading,hauling and crushing in mines.Adaptive neuro-fuzzy inference system (ANFIS) and radial basis function (RBF)show potentials for modeling the behavior of complex nonlinear processes such as those involved in fragmentation due to blasting of rocks.In this paper we developed ANFIS and RBF methods for modeling of sizing of rock fragmentation due to bench blasting by estimation of 80% passing size (K80) of Golgohar iron ore mine of Sir jan,Iran.Comparing the results of ANFIS and RBF models shows that although the statistical parameters RBF model is acceptable but the ANFIS proposed model is superior and also simpler because the ANFIS model is constructed using only two input parameters while seven input parameters used for construction of the RBF model.

  14. CONSTRUCTION OF POLYNOMIAL MATRIX USING BLOCK COEFFICIENT MATRIX REPRESENTATION AUTO-REGRESSIVE MOVING AVERAGE MODEL FOR ACTIVELY CONTROLLED STRUCTURES

    Institute of Scientific and Technical Information of China (English)

    LI Chunxiang; ZHOU Dai

    2004-01-01

    The polynomial matrix using the block coefficient matrix representation auto-regressive moving average (referred to as the PM-ARMA) model is constructed in this paper for actively controlled multi-degree-of-freedom (MDOF) structures with time-delay through equivalently transforming the preliminary state space realization into the new state space realization. The PM-ARMA model is a more general formulation with respect to the polynomial using the coefficient representation auto-regressive moving average (ARMA) model due to its capability to cope with actively controlled structures with any given structural degrees of freedom and any chosen number of sensors and actuators. (The sensors and actuators are required to maintain the identical number.) under any dimensional stationary stochastic excitation.

  15. Integration of Adaptive Neuro-Fuzzy Inference System, Neural Networks and Geostatistical Methods for Fracture Density Modeling

    Directory of Open Access Journals (Sweden)

    Ja’fari A.

    2014-01-01

    Full Text Available Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS and Neural Networks (NN algorithms for overall estimation of fracture density from conventional well log data. A simple averaging method was used to obtain a better result by combining results of ANFIS and NN. The algorithm applied on other wells of the field to obtain fracture density. In order to model the fracture density in the reservoir, we used variography and sequential simulation algorithms like Sequential Indicator Simulation (SIS and Truncated Gaussian Simulation (TGS. The overall algorithm applied to Asmari reservoir one of the SW Iranian oil fields. Histogram analysis applied to control the quality of the obtained models. Results of this study show that for higher number of fracture facies the TGS algorithm works better than SIS but in small number of fracture facies both algorithms provide approximately same results.

  16. Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization process

    OpenAIRE

    Petchinathan,G.; K. Valarmathi; Devaraj,D.; T. K. Radhakrishnan

    2014-01-01

    This paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT) and an adaptive neuro-fuzzy inference system (ANFIS). The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a G...

  17. Neuro-Fuzzy Prediction of Cooperation Interaction Profile of Flexible Road Train Based on Hybrid Automaton Modeling

    Directory of Open Access Journals (Sweden)

    Banjanovic-Mehmedovic Lejla

    2016-01-01

    Full Text Available Accurate prediction of traffic information is important in many applications in relation to Intelligent Transport systems (ITS, since it reduces the uncertainty of future traffic states and improves traffic mobility. There is a lot of research done in the field of traffic information predictions such as speed, flow and travel time. The most important research was done in the domain of cooperative intelligent transport system (C-ITS. The goal of this paper is to introduce the novel cooperation behaviour profile prediction through the example of flexible Road Trains useful road cooperation parameter, which contributes to the improvement of traffic mobility in Intelligent Transportation Systems. This paper presents an approach towards the control and cooperation behaviour modelling of vehicles in the flexible Road Train based on hybrid automaton and neuro-fuzzy (ANFIS prediction of cooperation profile of the flexible Road Train. Hybrid automaton takes into account complex dynamics of each vehicle as well as discrete cooperation approach. The ANFIS is a particular class of the ANN family with attractive estimation and learning potentials. In order to provide statistical analysis, RMSE (root mean square error, coefficient of determination (R2 and Pearson coefficient (r, were utilized. The study results suggest that ANFIS would be an efficient soft computing methodology, which could offer precise predictions of cooperative interactions between vehicles in Road Train, which is useful for prediction mobility in Intelligent Transport systems.

  18. Application of a New Hybrid Model with Seasonal Auto-Regressive Integrated Moving Average (ARIMA) and Nonlinear Auto-Regressive Neural Network (NARNN) in Forecasting Incidence Cases of HFMD in Shenzhen, China

    OpenAIRE

    Lijing Yu; Lingling Zhou; Li Tan; Hongbo Jiang; Ying Wang; Sheng Wei; Shaofa Nie

    2014-01-01

    BACKGROUND: Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic. METHOD: In this paper, a hybrid model combining seasonal auto-regressive integrated moving average...

  19. Temperature based daily incoming solar radiation modeling based on gene expression programming, neuro-fuzzy and neural network computing techniques.

    Science.gov (United States)

    Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.

    2012-04-01

    The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the

  20. Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization process

    Directory of Open Access Journals (Sweden)

    G. Petchinathan

    2014-06-01

    Full Text Available This paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT and an adaptive neuro-fuzzy inference system (ANFIS. The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a GUI developed in the MATLAB -SIMULINK platform. The main aim is to illustrate the online modelling and control of the experimental setup. The results of real-time control of an experimental pH process using the Internal Model Control (IMC strategy are also presented.

  1. Boundedly rational learning and heterogeneous trading strategies with hybrid neuro-fuzzy models

    NARCIS (Netherlands)

    S.D. Bekiros

    2009-01-01

    The present study deals with heterogeneous learning rules in speculative markets where heuristic strategies reflect the rules-of-thumb of boundedly rational investors. The major challenge for "chartists" is the development of new models that would enhance forecasting ability particularly for time se

  2. Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning

    Science.gov (United States)

    Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun

    2012-01-01

    With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy…

  3. Digital modelling of landscape and soil in a mountainous region: A neuro-fuzzy approach

    Science.gov (United States)

    Viloria, Jesús A.; Viloria-Botello, Alvaro; Pineda, María Corina; Valera, Angel

    2016-01-01

    Research on genetic relationships between soil and landforms has largely improved soil mapping. Recent technological advances have created innovative methods for modelling the spatial soil variation from digital elevation models (DEMs) and remote sensors. This generates new opportunities for the application of geomorphology to soil mapping. This study applied a method based on artificial neural networks and fuzzy clustering to recognize digital classes of land surfaces in a mountainous area in north-central Venezuela. The spatial variation of the fuzzy memberships exposed the areas where each class predominates, while the class centres helped to recognize the topographic attributes and vegetation cover of each class. The obtained classes of terrain revealed the structure of the land surface, which showed regional differences in climate, vegetation, and topography and landscape stability. The land-surface classes were subdivided on the basis of the geological substratum to produce landscape classes that additionally considered the influence of soil parent material. These classes were used as a framework for soil sampling. A redundancy analysis confirmed that changes of landscape classes explained the variation in soil properties (p = 0.01), and a Kruskal-Wallis test showed significant differences (p = 0.01) in clay, hydraulic conductivity, soil organic carbon, base saturation, and exchangeable Ca and Mg between classes. Thus, the produced landscape classes correspond to three-dimensional bodies that differ in soil conditions. Some changes of land-surface classes coincide with abrupt boundaries in the landscape, such as ridges and thalwegs. However, as the model is continuous, it disclosed the remaining variation between those boundaries.

  4. Methanol Reformer System Modeling and Control using an Adaptive Neuro-Fuzzy Inference System approach

    DEFF Research Database (Denmark)

    Justesen, Kristian Kjær; Ehmsen, Mikkel Præstholm; Andersen, John

    2012-01-01

    This work presents the experimental study and modelling of a methanol reformer system for a high temperature polymer electrolyte membrane (HTPEM) fuel cell stack. The analyzed system is a fully integrated HTPEM fuel cell system with a DC/DC control output able to be used as e.g. a mobile battery...... charger. The advantages of using a HTPEM methanol reformer is that the high quality waste heat can be used as a system heat input to heat and evaporate the input methanol/water mixture which afterwards is catalytically converted into a hydrogen rich gas usable in the high CO tolerant HTPEM fuel cells....... Creating a fuel cell system able to use a well known and easily distributable liquid fuel such as methanol is a good choice in some applications such as range extenders for electric vehicles as an alternative to compressed hydrogen. This work presents a control strategy called Current Correction...

  5. Neuro-fuzzy and model-based motion control for mobile manipulator among dynamic obstacles

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive Fuzzy Neural Controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.

  6. A mathematical model of neuro-fuzzy approximation in image classification

    Science.gov (United States)

    Gopalan, Sasi; Pinto, Linu; Sheela, C.; Arun Kumar M., N.

    2016-06-01

    Image digitization and explosion of World Wide Web has made traditional search for image, an inefficient method for retrieval of required grassland image data from large database. For a given input query image Content-Based Image Retrieval (CBIR) system retrieves the similar images from a large database. Advances in technology has increased the use of grassland image data in diverse areas such has agriculture, art galleries, education, industry etc. In all the above mentioned diverse areas it is necessary to retrieve grassland image data efficiently from a large database to perform an assigned task and to make a suitable decision. A CBIR system based on grassland image properties and it uses the aid of a feed-forward back propagation neural network for an effective image retrieval is proposed in this paper. Fuzzy Memberships plays an important role in the input space of the proposed system which leads to a combined neural fuzzy approximation in image classification. The CBIR system with mathematical model in the proposed work gives more clarity about fuzzy-neuro approximation and the convergence of the image features in a grassland image.

  7. Hybrid Neuro-Fuzzy Systems for Software Development Effort Estimation

    Directory of Open Access Journals (Sweden)

    Rama Sree P

    2012-12-01

    Full Text Available The major prevailing challenges for Software Projects are Software Estimations like cost estimation, effort estimation, quality estimation and risk analysis. Though there are several algorithmiccost estimation models in practice, each model has its own pros and cons for estimation. There is still a need to find a model that gives accurate estimates. This paper is an attempt to experiment different types of Neuro-Fuzzy Models. Using the types of Neuro-Fuzzy Models for software effort prediction is a relatively unexplored area. Two case studies are used for this purpose. The first is based on NASA-93dataset and the other is based on Maxwell-62 dataset. The case studies were analyzed using six different criterions like Variance Accounted For (VAF, Mean Absolute Relative Error (MARE, VarianceAbsolute Relative Error (VARE, Mean Balance Relative Error (Mean BRE, Mean Magnitude Relative Error (MMRE and Prediction. From the results and from reasoning, it is concluded that Type BCompensationNeuro-Fuzzy Model with more fuzzy rules is best suitable for cases in which the datapoints are more linear. Type J Neuro-Fuzzy Model with more fuzzy rules is best suitable for cases in which the datapoints are not linear.

  8. Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter.

    Science.gov (United States)

    Huang, Lei

    2015-09-30

    To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required.

  9. An application of Auto-regressive (AR model in predicting Aeroelastic Effectsof Lekki Cable Stayed Bridge

    Directory of Open Access Journals (Sweden)

    Hassan Abba Musa

    2016-06-01

    Full Text Available In current practice, the predictive analysis of stochastic problems encompasses a variety of statistical techniques from modeling, machine, and data mining that analyse current and historical facts to make predictions about future. Therefore, this research uses an AR Model whose codes are incorporated in the MATLAB software to predict possible aero-elastic effects of Lekki Bridge based on its existing parametric data and the conditions around the bridge. It was seen that, the fluctuating components of the wind velocity as displayed by the fluctuant curve will result in the vibration of the structure, even strengthening the resonance effect of the structure. Therefore, it suggested that, the natural frequency of the bridge should be set aside far from system frequency considering direct parametric excitation of pedestrian or vehicular traffic speed.

  10. Family system dynamics and type 1 diabetic glycemic variability: a vector-auto-regressive model.

    Science.gov (United States)

    Günther, Moritz Philipp; Winker, Peter; Böttcher, Claudia; Brosig, Burkhard

    2013-06-01

    Statistical approaches rooted in econometric methodology, so far foreign to the psychiatric and psychological realms have provided exciting and substantial new insights into complex mind-body interactions over time and individuals. Over 120 days, this structured diary study explored the mutual interactions of emotions within a classic 3-person family system with its Type 1 diabetic adolescent's daily blood glucose variability. Glycemic variability was measured through daily standard deviations of blood glucose determinations (at least 3 per day). Emotions were captured individually utilizing the self-assessment manikin on affective valence (negative-positive), activation (calm-excited), and control (dominated-dominant). Auto- and cross-correlating the stationary absolute (level) values of the mutually interacting parallel time series data sets through vector autoregression (VAR, grounded in econometric theory) allowed for the formulation of 2 concordant models. Applying Cholesky Impulse Response Analysis at a 95% confidence interval, we provided evidence for an adolescent being happy, calm, and in control to exhibit less glycemic variability and hence diabetic derailment. A nondominating mother and a happy father seemed to also reduce glycemic variability. Random shocks increasing glycemic variability affected only the adolescent and her father: In 1 model, the male parent felt in charge; in the other, he calmed down while his daughter turned sad. All reactions to external shocks lasted for less than 4 full days. Extant literature on affect and glycemic variability in Type 1 diabetic adolescents as well as challenges arising from introducing econometric theory to the field were discussed.

  11. A novel approach to equipment health management based on auto-regressive hidden semi-Markov model (AR-HSMM)

    Institute of Scientific and Technical Information of China (English)

    DONG Ming

    2008-01-01

    As a new maintenance method, CBM (condition based maintenance) is becoming more and more important for the health management of complicated and costly equipment. A prerequisite to widespread deployment of CBM technology and prac-tice in industry is effective diagnostics and prognostics. Recently, a pattern recog-nition technique called HMM (hidden Markov model) was widely used in many fields. However, due to some unrealistic assumptions, diagnositic results from HMM were not so good, and it was difficult to use HMM directly for prognosis. By relaxing the unrealistic assumptions in HMM, this paper presents a novel approach to equip-ment health management based on auto-regressive hidden semi-Markov model (AR-HSMM). Compared with HMM, AR-HSMM has three advantages: 1)It allows explicitly modeling the time duration of the hidden states and therefore is capable of prognosis. 2) It can relax observations' independence assumption by accom-modating a link between consecutive observations. 3) It does not follow the unre-alistic Markov chain's memoryless assumption and therefore provides more pow-erful modeling and analysis capability for real problems. To facilitate the computation in the proposed AR-HSMM-based diagnostics and prognostics, new forwardbackward variables are defined and a modified forward-backward algorithm is developed. The evaluation of the proposed methodology was carried out through a real world application case study: health diagnosis and prognosis of hydraulic pumps in Caterpillar Inc. The testing results show that the proposed new approach based on AR-HSMM is effective and can provide useful support for the decision-making in equipment health management.

  12. NEURO-FUZZY NETWORKS IN CAPP

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The neuro-fuzzy network (NFN) is used to model the rules and experience of the process planner.NFN is to select the manufacturing operations sequences for the part features. A detailed description of the NFN system development is given. The rule structure utilizes sigmoid functions to fuzzify the inputs, multiplication to combine the if part of the rules and summation to integrate the fired rules. Expert knowledge from previous process plans is used in determining the initial network structure and parameters of the membership functions. A back-propagation (BP) training algorithm was developed to fine tune the knowledge to company standards using the input-output data from executions of previous plans. The method is illustrated by an industrial example.

  13. Neuro-fuzzy modeling to predict physicochemical and microbiological parameters of partially dried cherry tomato during storage: effects on water activity, temperature and storage time.

    Science.gov (United States)

    Tao, Yang; Li, Yong; Zhou, Ruiyun; Chu, Dinh-Toi; Su, Lijuan; Han, Yongbin; Zhou, Jianzhong

    2016-10-01

    In the study, osmotically dehydrated cherry tomatoes were partially dried to water activity between 0.746 and 0.868, vacuum-packed and stored at 4-30 °C for 60 days. Adaptive neuro-fuzzy inference system (ANFIS) was utilized to predict the physicochemical and microbiological parameters of these partially dried cherry tomatoes during storage. Satisfactory accuracies were obtained when ANFIS was used to predict the lycopene and total phenolic contents, color and microbial contamination. The coefficients of determination for all the ANFIS models were higher than 0.86 and showed better performance for prediction compared with models developed by response surface methodology. Through ANFIS modeling, the effects of storage conditions on the properties of partially dried cherry tomatoes were visualized. Generally, contents of lycopene and total phenolics decreased with the increase in water activity, temperature and storage time, while aerobic plate count and number of yeasts and molds increased at high water activities and temperatures. Overall, ANFIS approach can be used as an effective tool to study the quality decrease and microbial pollution of partially dried cherry tomatoes during storage, as well as identify the suitable preservation conditions.

  14. Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5-dithiol onto gold nanoparticales-activated carbon

    Science.gov (United States)

    Ghaedi, M.; Hosaininia, R.; Ghaedi, A. M.; Vafaei, A.; Taghizadeh, F.

    2014-10-01

    In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope (SEM), Brunauer-Emmett-Teller (BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55 m2/g) and low pore size (activated carbon were 0.02 g adsorbent mass, 10 mg L-1 initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30 min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R2) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way.

  15. An Adaptive Neuro-Fuzzy Inference System Based Modeling for Corrosion-Damaged Reinforced HSC Beams Strengthened with External Glass Fibre Reinforced Polymer Laminates

    Directory of Open Access Journals (Sweden)

    P. N. Raghunath

    2012-01-01

    Full Text Available Problem statement: This study presents the results of ANFIS based model proposed for predicting the performance characteristics of reinforced HSC beams subjected to different levels of corrosion damage and strengthened with externally bonded glass fibre reinforced polymer laminates. Approach: A total of 21 beams specimens of size 150, 250×3000 mm were cast and tested. Results: Out of the 21 specimens, 7 specimens were tested without any corrosion damage (R-Series, 7 after inducing 10% corrosion damage (ASeries and another 7 after inducing 25% corrosion damage (B-Series. Out of the seven specimens in each series, one was tested without any laminate, three specimens were tested after applying 3 mm thick CSM, UDC and WR laminates and another three specimens after applying 5mm thick CSM, UDC and WR laminates. Conclusion/Recommendations: The test results show that the beams strengthened with externally bonded GFRP laminates exhibit increased strength, stiffness, ductility and composite action until failure. An Adaptive Neuro-Fuzzy Inference System (ANFIS model is developed for predicting the study parameters for input values lying within the range of this experimental study.

  16. Genetic algorithm-artificial neural network and adaptive neuro-fuzzy inference system modeling of antibacterial activity of annatto dye on Salmonella enteritidis.

    Science.gov (United States)

    Yolmeh, Mahmoud; Habibi Najafi, Mohammad B; Salehi, Fakhreddin

    2014-01-01

    Annatto is commonly used as a coloring agent in the food industry and has antimicrobial and antioxidant properties. In this study, genetic algorithm-artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict the effect of annatto dye on Salmonella enteritidis in mayonnaise. The GA-ANN and ANFIS were fed with 3 inputs of annatto dye concentration (0, 0.1, 0.2 and 0.4%), storage temperature (4 and 25°C) and storage time (1-20 days) for prediction of S. enteritidis population. Both models were trained with experimental data. The results showed that the annatto dye was able to reduce of S. enteritidis and its effect was stronger at 25°C than 4°C. The developed GA-ANN, which included 8 hidden neurons, could predict S. enteritidis population with correlation coefficient of 0.999. The overall agreement between ANFIS predictions and experimental data was also very good (r=0.998). Sensitivity analysis results showed that storage temperature was the most sensitive factor for prediction of S. enteritidis population.

  17. A New Neuro-Fuzzy Adaptive Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHU Lili; ZHANG Huanchun; JING Yazhi

    2003-01-01

    Novel neuro-fuzzy techniques are used to dynamically control parameter settings of genetic algorithms (GAs). The benchmark routine is an adaptive genetic algorithm (AGA) that uses a fuzzy knowledge-based system to control GA parameters. The self-learning ability of the cerebellar model ariculation controller(CMAC) neural network makes it possible for on-line learning the knowledge on GAs throughout the run. Automatically designing and tuning the fuzzy knowledge-base system, neurofuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learning method. The Results from initial experiments show a Dynamic Parametric AGA system designed by the proposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a wide range of combinatorial optimization.

  18. Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5-dithiol onto gold nanoparticales-activated carbon.

    Science.gov (United States)

    Ghaedi, M; Hosaininia, R; Ghaedi, A M; Vafaei, A; Taghizadeh, F

    2014-10-15

    In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope(SEM), Brunauer-Emmett-Teller(BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55m(2)/g) and low pore size (particle size lower than 48.8Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02g adsorbent mass, 10mgL(-1) initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R(2)) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way.

  19. Estimating the crowding level with a neuro-fuzzy classifier

    Science.gov (United States)

    Boninsegna, Massimo; Coianiz, Tarcisio; Trentin, Edmondo

    1997-07-01

    This paper introduces a neuro-fuzzy system for the estimation of the crowding level in a scene. Monitoring the number of people present in a given indoor environment is a requirement in a variety of surveillance applications. In the present work, crowding has to be estimated from the image processing of visual scenes collected via a TV camera. A suitable preprocessing of the images, along with an ad hoc feature extraction process, is discussed. Estimation of the crowding level in the feature space is described in terms of a fuzzy decision rule, which relies on the membership of input patterns to a set of partially overlapping crowding classes, comprehensive of doubt classifications and outliers. A society of neural networks, either multilayer perceptrons or hyper radial basis functions, is trained to model individual class-membership functions. Integration of the neural nets within the fuzzy decision rule results in an overall neuro-fuzzy classifier. Important topics concerning the generalization ability, the robustness, the adaptivity and the performance evaluation of the system are explored. Experiments with real-world data were accomplished, comparing the present approach with statistical pattern recognition techniques, namely linear discriminant analysis and nearest neighbor. Experimental results validate the neuro-fuzzy approach to a large extent. The system is currently working successfully as a part of a monitoring system in the Dinegro underground station in Genoa, Italy.

  20. Dynamic Modeling of a Reformed Methanol Fuel Cell System using Empirical Data and Adaptive Neuro-Fuzzy Inference System Models

    DEFF Research Database (Denmark)

    Justesen, Kristian Kjær; Andreasen, Søren Juhl; Shaker, Hamid Reza

    2014-01-01

    In this work, a dynamic MATLAB Simulink model of a H3-350 Reformed Methanol Fuel Cell (RMFC) stand-alone battery charger produced by Serenergy is developed on the basis of theoretical and empirical methods. The advantage of RMFC systems is that they use liquid methanol as a fuel instead of gaseous...... of the reforming process are implemented. Models of the cooling flow of the blowers for the fuel cell and the burner which supplies process heat for the reformer are made. The two blowers have a common exhaust, which means that the two blowers influence each other’s output. The models take this into account using...... an empirical approach. Fin efficiency models for the cooling effect of the air are also developed using empirical methods. A fuel cell model is also implemented based on a standard model which is adapted to fit the measured performance of the H3-350 module. All the individual parts of the model are verified...

  1. Dynamic Modeling of a Reformed Methanol Fuel Cell System using Empirical Data and Adaptive Neuro-Fuzzy Inference System Models

    DEFF Research Database (Denmark)

    Justesen, Kristian Kjær; Andreasen, Søren Juhl; Shaker, Hamid Reza

    2013-01-01

    In this work, a dynamic MATLAB Simulink model of a H3-350 Reformed Methanol Fuel Cell (RMFC) stand-alone battery charger produced by Serenergy is developed on the basis of theoretical and empirical methods. The advantage of RMFC systems is that they use liquid methanol as a fuel instead of gaseous...... of the reforming process are implemented. Models of the cooling flow of the blowers for the fuel cell and the burner which supplies process heat for the reformer are made. The two blowers have a common exhaust, which means that the two blowers influence each other’s output. The models take this into account using...... an empirical approach. Fin efficiency models for the cooling effect of the air are also developed using empirical methods. A fuel cell model is also implemented based on a standard model which is adapted to fit the measured performance of the H3-350 module. All the individual parts of the model are verified...

  2. HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.

    Science.gov (United States)

    Kim, J; Kasabov, N

    1999-11-01

    This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.

  3. Application of adaptive neuro-fuzzy interference system models for prediction of forest fires in the usa on the basis of solar activity

    Directory of Open Access Journals (Sweden)

    Radovanović Milan M.

    2015-01-01

    Full Text Available In this research we search for a functional dependence between the occurrence of forest fires in the USA and the factors which characterize the solar activity. For this purpose we used several methods (R/S analysis, Hurst index to establish potential links between the influx of some parameters from the sun and the occurrence of forest fires with lag of several days. We found evidence for a connection and developed a prognostic scenario based on the Adaptive neuro-fuzzy interference system (ANFIS technique. This scenario allows the prediction between 79-93% of forest fires. [Projekat Ministarstva nauke Republike Srbije, br. III47007

  4. Fuzzy Clustering, Genetic Algorithms and Neuro-Fuzzy Methods Compared for Hybrid Fuzzy-First Principles Modeling

    NARCIS (Netherlands)

    Lith, Pascal F. van; Betlem, Ben H.L.; Roffel, Brian

    2002-01-01

    Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and transfe

  5. 年径流预测的遗传门限自回归模型%Genetic Threshold Auto-Regressive Model for Predicting Annual Ruu-Off

    Institute of Scientific and Technical Information of China (English)

    金菊良; 杨晓华; 丁晶

    2001-01-01

    为有效利用年径流时间序列资料所隐含的时序分段相依性这一重要信息,提出了用门限自回归模型(TAR)来预测年径流,并研制了TAR建模的一整套简便通用的方案。用所提出的改进遗传算法,可同时优化门限值和自回归系数,从而解决了TAR建模过程所涉及的大量复杂寻优工作这一难题,为TAR模型的广泛应用提供了强有力的工具。实例计算的结果说明这套方案是可行的和有效的;通过门限值的控制作用,TAR模型可以有效地限制模型误差,从而保证TAR模型预测性能的稳健性,提高预测精度。该方案具有通用性,在非线性时序预测中具有重要的理论意义和实用价值。%To effectively utilize the important information of the section interdependence during the time series of annual run-off,threshold auto-regressive(TAR) model is suggested to predict annual runoff.A simple and general scheme is presented for establishing TAR model.With the improved genetic algorithm by the authors,both of threshold values and auto-regressive coefficients can be optimized ,and the difficulty problem of modeling of TAR is resolved,which gives a strong tool for widely applying TAR model.The case study shows that the scheme is practical and efficient,and that TAR model can successfully reduce model errors,and ensure good stability and accuracy of the model forecasting by controlling threshold valves.As a general method,the scheme has major theoretic valve and wide-ranging application for predicting of nonlinear time series.

  6. Neuro-fuzzy predictive control for nonlinear application

    Institute of Scientific and Technical Information of China (English)

    CHEN Dong-xiang; WANG Gang; LV Shi-xia

    2008-01-01

    Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. The neuro-fuzzy predictor has the capability of achieving high predictive accuracy due to its nonlinear mapping and interpolation features, and adaptively updating network parameters by a learning procedure to re-duce the model errors caused by changes of the process under control. To cope with the difficult problem of non-linear optimization, Pepanaqi method was applied to search the optimal or suboptimal solution. Comparisons were made among the objective function values of alternatives in initial space. The search was then confined to shrink the smaller region according to results of comparisons. The convergent point was finally approached to be considered as the optimal or suboptimal solution. Experimental results of the neuro-fuzzy predictive control for drier application reveal that the proposed control scheme has less tracking errors and can smooth control actions, which is applicable to changes of drying condition.

  7. Fouling resistance prediction using artificial neural network nonlinear auto-regressive with exogenous input model based on operating conditions and fluid properties correlations

    Science.gov (United States)

    Biyanto, Totok R.

    2016-06-01

    Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO2 emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model are flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.

  8. AN INTELLIGENT NEURO-FUZZY TERMINAL SLIDING MODE CONTROL METHOD WITH APPLICATION TO ATOMIC FORCE MICROSCOPE

    Directory of Open Access Journals (Sweden)

    Seied Yasser Nikoo

    2016-11-01

    Full Text Available In this paper, a neuro-fuzzy fast terminal sliding mode control method is proposed for controlling a class of nonlinear systems with bounded uncertainties and disturbances. In this method, a nonlinear terminal sliding surface is firstly designed. Then, this sliding surface is considered as input for an adaptive neuro-fuzzy inference system which is the main controller. A proportinal-integral-derivative controller is also used to asist the neuro-fuzzy controller in order to improve the performance of the system at the begining stage of control operation. In addition, bee algorithm is used in this paper to update the weights of neuro-fuzzy system as well as the parameters of the proportinal-integral-derivative controller. The proposed control scheme is simulated for vibration control in a model of atomic force microscope system and the results are compared with conventional sliding mode controllers. The simulation results show that the chattering effect in the proposed controller is decreased in comparison with the sliding mode and the terminal sliding mode controllers. Also, the method provides the advantages of fast convergence and low model dependency compared to the conventional methods.

  9. The Prediction of Exchange Rates with the Use of Auto-Regressive Integrated Moving-Average Models

    Directory of Open Access Journals (Sweden)

    Daniela Spiesová

    2014-10-01

    Full Text Available Currency market is recently the largest world market during the existence of which there have been many theories regarding the prediction of the development of exchange rates based on macroeconomic, microeconomic, statistic and other models. The aim of this paper is to identify the adequate model for the prediction of non-stationary time series of exchange rates and then use this model to predict the trend of the development of European currencies against Euro. The uniqueness of this paper is in the fact that there are many expert studies dealing with the prediction of the currency pairs rates of the American dollar with other currency but there is only a limited number of scientific studies concerned with the long-term prediction of European currencies with the help of the integrated ARMA models even though the development of exchange rates has a crucial impact on all levels of economy and its prediction is an important indicator for individual countries, banks, companies and businessmen as well as for investors. The results of this study confirm that to predict the conditional variance and then to estimate the future values of exchange rates, it is adequate to use the ARIMA (1,1,1 model without constant, or ARIMA [(1,7,1,(1,7] model, where in the long-term, the square root of the conditional variance inclines towards stable value.

  10. Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system

    Science.gov (United States)

    Kim, Chan Moon; Parnichkun, Manukid

    2017-02-01

    Coagulation is an important process in drinking water treatment to attain acceptable treated water quality. However, the determination of coagulant dosage is still a challenging task for operators, because coagulation is nonlinear and complicated process. Feedback control to achieve the desired treated water quality is difficult due to lengthy process time. In this research, a hybrid of k-means clustering and adaptive neuro-fuzzy inference system (k-means-ANFIS) is proposed for the settled water turbidity prediction and the optimal coagulant dosage determination using full-scale historical data. To build a well-adaptive model to different process states from influent water, raw water quality data are classified into four clusters according to its properties by a k-means clustering technique. The sub-models are developed individually on the basis of each clustered data set. Results reveal that the sub-models constructed by a hybrid k-means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. Therefore, the hybrid model of k-means-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously.

  11. Adaptive Neuro-fuzzy approach in friction identification

    Science.gov (United States)

    Zaiyad Muda @ Ismail, Muhammad

    2016-05-01

    Friction is known to affect the performance of motion control system, especially in terms of its accuracy. Therefore, a number of techniques or methods have been explored and implemented to alleviate the effects of friction. In this project, the Artificial Intelligent (AI) approach is used to model the friction which will be then used to compensate the friction. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is chosen among several other AI methods because of its reliability and capabilities of solving complex computation. ANFIS is a hybrid AI-paradigm that combines the best features of neural network and fuzzy logic. This AI method (ANFIS) is effective for nonlinear system identification and compensation and thus, being used in this project.

  12. Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

    Science.gov (United States)

    Khoshbin, Fatemeh; Bonakdari, Hossein; Hamed Ashraf Talesh, Seyed; Ebtehaj, Isa; Zaji, Amir Hossein; Azimi, Hamed

    2016-06-01

    In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error = 3.362 and root mean square error = 0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron-artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.

  13. CENTRIC MANAGEMENT SYSTEM BASED ON NEURO - FUZZY TOPOLOGY

    Directory of Open Access Journals (Sweden)

    Shumkov Y. A.

    2014-11-01

    Full Text Available The article describes the network-centric approach to a building control system based on the "inner teacher" neuro - fuzzy topology, which uses the principles of reinforcement learning

  14. Simulink-based HW/SW codesign of embedded neuro-fuzzy systems.

    Science.gov (United States)

    Reyneri, L M; Chiaberge, M; Lavagno, L

    2000-06-01

    We propose a semi-automatic HW/SW codesign flow for low-power and low-cost Neuro-Fuzzy embedded systems. Applications range from fast prototyping of embedded systems to high-speed simulation of Simulink models and rapid design of Neuro-Fuzzy devices. The proposed codesign flow works with different technologies and architectures (namely, software, digital and analog). We have used The Mathworks' Simulink environment for functional specification and for analysis of performance criteria such as timing (latency and throughput), power dissipation, size and cost. The proposed flow can exploit trade-offs between SW and HW as well as between digital and analog implementations, and it can generate, respectively, the C, VHDL and SKILL codes of the selected architectures.

  15. Edge Detection with Neuro-Fuzzy Approach in Digital Synthesis Images

    Directory of Open Access Journals (Sweden)

    Fatma ZRIBI

    2016-04-01

    Full Text Available This paper presents an enhanced Neuro-Fuzzy (NF Approach of edge detection with an analysis of the characteristic of the method. The specificity of our method is an enhancement of the learning database of the diagonal edges compared to the original learning database. The original inspired NF edge detection model uses just one image learning database realized by Emin Yuksel. The tests are accomplished in synthesis images with a noised one of 20% of Gaussian noise.

  16. Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

    Science.gov (United States)

    Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.

    2016-11-01

    All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.

  17. Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

    Science.gov (United States)

    Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.

    2016-02-01

    All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.

  18. Evaluating Loans Using a Combination of Data Envelopment and Neuro-Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Rashmi Malhotra

    2015-02-01

    Full Text Available A business organization's objective is to make better decisions at all levels of the firm to improve performance. Typically organizations are multi-faceted and complex systems that use uncertain information. Therefore, making quality decisions to improve organizational performance is a daunting task. Organizations use decision support systems that apply different business intelligence techniques such as statistical models, scoring models, neural networks, expert systems, neuro-fuzzy systems, case-based systems, or simply rules that have been developed through experience. Managers need a decision-making approach that is robust, competent, effective, efficient, and integrative to handle the multi-dimensional organizational entities. The decision maker deals with multiple players in an organization such as products, customers, competitors, location, geographic structure, scope, internal organization, and cultural dimension [46]. Sound decisions include two important concepts: efficiency (return on invested resources and effectiveness (reaching predetermined goals. However, quite frequently, the decision maker cannot simultaneously handle data from different sources. Hence, we recommend that managers analyze different aspects of data from multiple sources separately and integrate the results of the analysis. This study proposes the design of a multi-attribute-decision-support-system that combines the analytical power of two different tools: data envelopment analysis (DEA and fuzzy logic. DEA evaluates and measures the relative efficiency of decision making units that use multiple inputs and outputs to provide non-objective measures without making any specific assumptions about data. On the other hand fuzzy logic's main strength lies in handling imprecise data. This study proposes a modeling technique that jointly uses the two techniques to benefit from the two methodologies. A major advantage of the DEA approach is that it clearly identifies the

  19. Adaptive neuro-fuzzy controller of switched reluctance motor

    Directory of Open Access Journals (Sweden)

    Tahour Ahmed

    2007-01-01

    Full Text Available This paper presents an application of adaptive neuro-fuzzy (ANFIS control for switched reluctance motor (SRM speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI.

  20. A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems

    CERN Document Server

    Mahdaoui, Rafik; Mouss, Mohamed Djamel; Chouhal, Ouahiba

    2011-01-01

    Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures....

  1. Development of quantum-based adaptive neuro-fuzzy networks.

    Science.gov (United States)

    Kim, Sung-Suk; Kwak, Keun-Chang

    2010-02-01

    In this study, we are concerned with a method for constructing quantum-based adaptive neuro-fuzzy networks (QANFNs) with a Takagi-Sugeno-Kang (TSK) fuzzy type based on the fuzzy granulation from a given input-output data set. For this purpose, we developed a systematic approach in producing automatic fuzzy rules based on fuzzy subtractive quantum clustering. This clustering technique is not only an extension of ideas inherent to scale-space and support-vector clustering but also represents an effective prototype that exhibits certain characteristics of the target system to be modeled from the fuzzy subtractive method. Furthermore, we developed linear-regression QANFN (LR-QANFN) as an incremental model to deal with localized nonlinearities of the system, so that all modeling discrepancies can be compensated. After adopting the construction of the linear regression as the first global model, we refined it through a series of local fuzzy if-then rules in order to capture the remaining localized characteristics. The experimental results revealed that the proposed QANFN and LR-QANFN yielded a better performance in comparison with radial basis function networks and the linguistic model obtained in previous literature for an automobile mile-per-gallon prediction, Boston Housing data, and a coagulant dosing process in a water purification plant.

  2. Estimation and Approximation Using Neuro-Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Nidhi Arora

    2016-06-01

    Full Text Available Estimation and Approximation plays an important role in planning for future. People especially the business leaders, who understand the significance of estimation, practice it very often. The act of estimation or approximation involves analyzing historical data pertaining to domain, current trends and expectations of people connected to it. Exercising estimation is not only complicated due to technological change in the world around, but also due to complexity of the problems. Traditional numerical based techniques for solution of ill-defined non-linear real world problems are not sufficient. Hence, there is a need of some robust methodologies which can deal with dynamic environment, imprecise facts and uncertainty in the available data to achieve practical applicability at low cost. Soft computing seeks to solve class of problems not suited for traditional algorithmic approaches. To address the common problems in business of inexactness, some models are put forward for servicing, support and monitoring by approximating and estimating important outcomes. This work illustrates some very general yet widespread problems which are of interest to common people. The suggested approaches can overcome the fuzziness in traditional methods by predicting some future events and getting better control on business. This includes study of various neuro-fuzzy architectures and their possible applications in various areas, where decision-making using classical methods fail.

  3. Kepler AutoRegressive Planet Search: Motivation & Methodology

    Science.gov (United States)

    Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian

    2015-08-01

    The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Auto-Regressive Moving-Average (ARMA) models, Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and related models are flexible, phenomenological methods used with great success to model stochastic temporal behaviors in many fields of study, particularly econometrics. Powerful statistical methods are implemented in the public statistical software environment R and its many packages. Modeling involves maximum likelihood fitting, model selection, and residual analysis. These techniques provide a useful framework to model stellar variability and are used in KARPS with the objective of reducing stellar noise to enhance opportunities to find as-yet-undiscovered planets. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; ARMA-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. We apply the procedures to simulated Kepler-like time series with known stellar and planetary signals to evaluate the effectiveness of the KARPS procedures. The ARMA-type modeling is effective at reducing stellar noise, but also reduces and transforms the transit signal into ingress/egress spikes. A periodogram based on the TCF is constructed to concentrate the signal

  4. Research on Modeling with Adaptive Neuro-Fuzzy Inference System%自适应神经模糊推理系统建模研究

    Institute of Scientific and Technical Information of China (English)

    鲁斌; 何华灿

    2003-01-01

    With rapid development of the fuzzy control application field, the existing system for fuzzy inferring modeling cannot more and more suit the requirements of fuzzy control. About how to apply the theories of fuzzy control to practice rapidly and conveniently, this paper presents a reasonable and practical method, which supports all sorts of fuzzy inferring system of MAMDANI and SUGENO to be modeled not only by tuning references of membership functions, but also by tuning fuzzy inferring structure. The modeling instance shows that it's practical and effective.

  5. Principal component analysis- adaptive neuro-fuzzy inference system modeling and genetic algorithm optimization of adsorption of methylene blue by activated carbon derived from Pistacia khinjuk.

    Science.gov (United States)

    Ghaedi, M; Ghaedi, A M; Abdi, F; Roosta, M; Vafaei, A; Asghari, A

    2013-10-01

    In the present study, activated carbon (AC) simply derived from Pistacia khinjuk and characterized using different techniques such as SEM and BET analysis. This new adsorbent was used for methylene blue (MB) adsorption. Fitting the experimental equilibrium data to various isotherm models shows the suitability and applicability of the Langmuir model. The adsorption mechanism and rate of processes was investigated by analyzing time dependency data to conventional kinetic models and it was found that adsorption follow the pseudo-second-order kinetic model. Principle component analysis (PCA) has been used for preprocessing of input data and genetic algorithm optimization have been used for prediction of adsorption of methylene blue using activated carbon derived from P. khinjuk. In our laboratory various activated carbon as sole adsorbent or loaded with various nanoparticles was used for removal of many pollutants (Ghaedi et al., 2012). These results indicate that the small amount of proposed adsorbent (1.0g) is applicable for successful removal of MB (RE>98%) in short time (45min) with high adsorption capacity (48-185mgg(-1)).

  6. Designing Flexible Neuro-Fuzzy System Based on Sliding Mode Controller for Magnetic Levitation Systems

    Directory of Open Access Journals (Sweden)

    Zahra Mohammadi

    2011-07-01

    Full Text Available This study presents a novel controller of magnetic levitation system by using new neuro-fuzzy structures which called flexible neuro-fuzzy systems. In this type of controller we use sliding mode control with neuro-fuzzy to eliminate the Jacobian of plant. At first, we control magnetic levitation system with Mamdanitype neuro-fuzzy systems and logical-type neuro-fuzzy systems separately and then we use two types of flexible neuro-fuzzy systems as controllers. Basic flexible OR-type neuro-fuzzy inference system and basic compromise AND-type neuro-fuzzy inference system are two new flexible neuro-fuzzy controllers which structure of fuzzy inference system (Mamdani or logical is determined in the learning process. We can investigate with these two types of controllers which of the Mamdani or logical type systems has better performance for control of this plant. Finally we compare performance of these controllers with sliding mode controller and RBF sliding mode controller.

  7. Adaptive Neuro-fuzzy Controller Design for Non-affine Nonlinear Systems

    Institute of Scientific and Technical Information of China (English)

    JIA Li; GE Shu-zhi; QIU Ming-sen

    2008-01-01

    An adaptive neuro-fuzzy control is investigated for a class of noa-affine nonlinear systems.To do so,rigorous description and quantification of the approximation error of the neuro-fuzzy controller are firstly discussed.Applying this result and Lyapunov stability theory,a novel updating algorithm to adapt the weights,centers,and widths of the neuro-fuzzy controller is presented.Consequently,the proposed design method is able to guaranteg the stability of the closed-loop system and the convergence of the tracking error.Simulation results illustrate the effectiveness of the proposed adaptive neuro-fuzzy control scheme.

  8. Neuro-fuzzy controller to navigate an unmanned vehicle.

    Science.gov (United States)

    Selma, Boumediene; Chouraqui, Samira

    2013-12-01

    A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN).

  9. A novel Neuro-fuzzy classification technique for data mining

    Directory of Open Access Journals (Sweden)

    Soumadip Ghosh

    2014-11-01

    Full Text Available In our study, we proposed a novel Neuro-fuzzy classification technique for data mining. The inputs to the Neuro-fuzzy classification system were fuzzified by applying generalized bell-shaped membership function. The proposed method utilized a fuzzification matrix in which the input patterns were associated with a degree of membership to different classes. Based on the value of degree of membership a pattern would be attributed to a specific category or class. We applied our method to ten benchmark data sets from the UCI machine learning repository for classification. Our objective was to analyze the proposed method and, therefore compare its performance with two powerful supervised classification algorithms Radial Basis Function Neural Network (RBFNN and Adaptive Neuro-fuzzy Inference System (ANFIS. We assessed the performance of these classification methods in terms of different performance measures such as accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, precision, recall, and f-measure. In every aspect the proposed method proved to be superior to RBFNN and ANFIS algorithms.

  10. Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition

    Directory of Open Access Journals (Sweden)

    Mohammad Subhi Al-batah

    2014-01-01

    Full Text Available To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL and high-grade squamous intraepithelial lesion (HSIL. The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.

  11. Neuro-fuzzy quantification of personal perceptions of facial images based on a limited data set.

    Science.gov (United States)

    Diago, Luis; Kitaoka, Tetsuko; Hagiwara, Ichiro; Kambayashi, Toshiki

    2011-12-01

    Artificial neural networks are nonlinear techniques which typically provide one of the most accurate predictive models perceiving faces in terms of the social impressions they make on people. However, they are often not suitable to be used in many practical application domains because of their lack of transparency and comprehensibility. This paper proposes a new neuro-fuzzy method to investigate the characteristics of the facial images perceived as Iyashi by one hundred and fourteen subjects. Iyashi is a Japanese word used to describe a peculiar phenomenon that is mentally soothing, but is yet to be clearly defined. In order to gain a clear insight into the reasoning made by the nonlinear prediction models such as holographic neural networks (HNN) in the classification of Iyashi expressions, the interpretability of the proposed fuzzy-quantized HNN (FQHNN) is improved by reducing the number of input parameters, creating membership functions and extracting fuzzy rules from the responses provided by the subjects about a limited dataset of 20 facial images. The experimental results show that the proposed FQHNN achieves 2-8% increase in the prediction accuracy compared with traditional neuro-fuzzy classifiers while it extracts 35 fuzzy rules explaining what characteristics a facial image should have in order to be classified as Iyashi-stimulus for 87 subjects.

  12. UAV Controller Based on Adaptive Neuro-Fuzzy Inference System and PID

    Directory of Open Access Journals (Sweden)

    Ali Moltajaei Farid

    2013-01-01

    Full Text Available ANFIS is combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system, capable of reasoning and learning in an uncertain and imprecise environment. In this paper, an adaptive neuro-fuzzy inference system (ANFIS is employed to control an unmanned aircraft vehicle (UAV.  First, autopilots structure is defined, and then ANFIS controller is applied, to control UAVs lateral position. The results of ANFIS and PID lateral controllers are compared, where it shows the two controllers have similar results. ANFIS controller is capable to adaptation in nonlinear conditions, while PID has to be tuned to preserves proper control in some conditions. The simulation results generated by Matlab using Aerosim Aeronautical Simulation Block Set, which provides a complete set of tools for development of six degree-of-freedom. Nonlinear Aerosonde unmanned aerial vehicle model with ANFIS controller is simulated to verify the capability of the system. Moreover, the results are validated by FlightGear flight simulator.

  13. Neuro fuzzy control of the FES assisted freely swinging leg of paraplegic subjects

    NARCIS (Netherlands)

    Spek, van der Jaap H.; Velthuis, Wubbe J.R.; Veltink, Peter H.; Vries, de Theo J.A.

    1996-01-01

    The authors designed a neuro fuzzy control strategy for control of cyclical leg movements of paraplegic subjects. The cyclical leg movements were specified by three `swing phase objectives', characteristic of natural human gait. The neuro fuzzy controller is a combination of a fuzzy logic controller

  14. Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach

    Science.gov (United States)

    Verma, Akhilesh K.; Chaki, Soumi; Routray, Aurobinda; Mohanty, William K.; Jenamani, Mamata

    2014-12-01

    In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. In this paper, we opt for ANN and three different categories of Adaptive Neuro-Fuzzy Inference System (ANFIS) based on clustering of the available datasets. A comparative analysis of these three different NF models (i.e., Sugeno-type fuzzy inference systems using a grid partition on the data (Model 1), using subtractive clustering (Model 2), and using Fuzzy c-means (FCM) clustering (Model 3)) and ANN suggests that Model 3 has outperformed its counterparts in terms of performance evaluators on the present dataset. Performance of the selected algorithms is evaluated in terms of correlation coefficients (CC), root

  15. Direct-Torque Neuro-Fuzzy Control of Induction Motor

    Institute of Scientific and Technical Information of China (English)

    徐君鹏; CHEN Yan-feng; LI Guo-hou

    2007-01-01

    Fuzzy systems are currently being used in a wide field of industrial and scientific applications. Since the design and especially the optimization process of fuzzy systems can be very time consuming, it is convenient to have algorithms which construct and optimize them automatically. In order to improve the system stability and raise the response speed, a new control scheme, direct-torque neuro-fuzzy control for induction motor drive, was put forward. The design and tuning procedure have been described. Also, the improved stator flux estimation algorithm, which guarantees eccentric estimated flux has been proposed.

  16. Neuro-fuzzy generalized predictive control of boiler steam temperature

    Institute of Scientific and Technical Information of China (English)

    Xiangjie LIU; Jizhen LIU; Ping GUAN

    2007-01-01

    Power plants are nonlinear and uncertain complex systems.Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant.A nonlinear generalized predictive controller based on neuro-fuzzy network(NFGPC)is proposed in this paper.The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant.From the experiments on the plant and the simulation of the plant,much better performance than the traditional controller is obtained.

  17. Adaptive Neuro-Fuzzy Extended Kalman Filtering for Robot Localization

    Directory of Open Access Journals (Sweden)

    Ramazan Havangi

    2010-03-01

    Full Text Available Extended Kalman Filter (EKF has been a popular approach to localization a mobile robot. However, the performance of the EKF and the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices (Qk and Rk , respectively. Imprecise knowledge of these statistics can cause significant degradation in performance. This paper proposed the development of an Adaptive Neuro- Fuzzy Extended Kalman Filtering (ANFEKF for localization of robot. The Adaptive Neuro-Fuzzy attempts to estimate the elements of Qk and Rk matrices of the EKF algorithm, at each sampling instant when measurement update step is carried out. The ANFIS supervises the performance of the EKF with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (SD to minimize the differences of the actual value of the covariance of the residual with its theoretical value as much possible. The simulation results show the effectiveness of the proposed algorithm.

  18. 改进模糊自回归模型在预测网络接通率中的应用%Improved fuzzy auto-regressive model for connection rate prediction

    Institute of Scientific and Technical Information of China (English)

    申晨; 孙永雄; 黄丽平; 刘李蓬; 李树秋

    2013-01-01

    针对通信网络中性能指标预测的需要,提出了基于改进的模糊自回归模型的接通率预测方法,研究了拟合度门限自适应的模糊自回归模型.将中值滤波应用于模糊自回归模型的数据预处理中,在此基础上,针对部分应用拟合度门限不明确的特点,将拟合度门限计算式加入预测模型中,实现模型拟合度门限的自适应.仿真实验表明:基于Fuzzy AR模型的预测方法可以用于对接通率的预测,预测结果拟合度较高.%Specific to the need of performance prediction in communication networks, a connection rate prediction method based on fuzzy Auto-Regressive (AR) model was proposed and improved, and the fuzzy AR model based on adaptive fitting degree threshold was studied. The median filtering method was applied to pre-process the data of fuzzy AR model. On this basis, for the uncertain thresholds of some applications, the fitting degree threshold formula was added to the prediction model to make it adaptive. The simulation results show that the predistion method based on fuzzy AR model can be used to predict the connection rate with a higher fitting degree.q

  19. APPLICATION OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM IN INTEREST RATES EFFECTS ON STOCK RETURNS

    Directory of Open Access Journals (Sweden)

    ELEFTHERIOS GIOVANIS

    2011-02-01

    Full Text Available In the current study we examine the effects of interest rate changes on common stock returns of Greek banking sector. We examine theGeneralized Autoregressive Heteroskedasticity (GARCH process and an Adaptive Neuro-Fuzzy Inference System (ANFIS. The conclusions of our findings are that the changes of interest rates, based on GARCH model, are insignificant on common stock returns during the period we examine. On the other hand, with ANFIS we can get the rules and in each case we can have positive or negative effects depending on the conditions and the firing rules of inputs, which information is not possible to be retrieved with the traditional econometric modelling. Furthermore we examine the forecasting performance of both models and we conclude that ANFIS outperforms GARCH model in both in-sample and out-of-sample periods.

  20. Prediction of photonic crystal fiber characteristics by Neuro-Fuzzy system

    Science.gov (United States)

    Pourmahyabadi, M.; Mohammad Nejad, S.

    2009-10-01

    The most common methods applied in the analysis of photonic crystal fibers (PCFs) are finite difference time/frequency domain (FDTD/FDFD) method and finite element method (FEM). These methods are very general and reliable (well tested). They describe arbitrary structure but are numerically intensive and require detailed treatment of boundaries and complex definition of calculation mesh. So these conventional models that simulate the photonic response of PCFs are computationally expensive and time consuming. Therefore, a practical design process with trial and error cannot be done in a reasonable amount of time. In this article, an artificial intelligence method such as Neuro-Fuzzy system is used to establish a model that can predict the properties of PCFs. Simulation results show that this model is remarkably effective in predicting the properties of PCF such as dispersion, dispersion slope and loss over the C communication band.

  1. A Neuro-Fuzzy System for Extracting Environment Features Based on Ultrasonic Sensors

    Directory of Open Access Journals (Sweden)

    Evelio José González

    2009-12-01

    Full Text Available In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case.

  2. A neuro-fuzzy controller for xenon spatial oscillations in load-following operation

    Energy Technology Data Exchange (ETDEWEB)

    Na, Man Gyun [Chosun University, Kwangju (Korea, Republic of); Upadhyaya, Belle R. [The University of Tennessee, Knoxville (United States)

    1997-12-31

    A neuro-fuzzy control algorithm is applied for xenon spatial oscillations in a pressurized water reactor. The consequent and antecedent parameters of the fuzzy rules are tuned by the gradient descent method. The reactor model used for computer simulations is a two-point xenon oscillation model. The reactor core is axially divided into two regions and each region has one input and one output and is coupled with the other region. The interaction between the regions of the reactor core is treated by a decoupling scheme. This proposed control method exhibits very responses to a step or a ramp change of target axial offest without any residual flux oscillations. 9 refs., 5 figs. (Author)

  3. Prediction of Rotor Spun Yarn Strength Using Adaptive Neuro-fuzzy Inference System and Linear Multiple Regression Methods

    Institute of Scientific and Technical Information of China (English)

    NURWAHA Deogratias; WANG Xin-hou

    2008-01-01

    This paper presents a comparison study of two models for predicting the strength of rotor spun cotton yarns from fiber properties. The adaptive neuro-fuzzy system inference (ANFIS) and Multiple Linear Regression models are used to predict the rotor spun yarn strength. Fiber properties and yarn count are used as inputs to train the two models and the count-strength-product (CSP) was the target. The predictive performances of the two models are estimated and compared. We found that the ANFIS has a better predictive power in comparison with linear multipleregression model. The impact of each fiber property is also illustrated.

  4. New concept of direct torque neuro-fuzzy control for induction motor drives. Simulation study

    Energy Technology Data Exchange (ETDEWEB)

    Grabowski, P.Z. [Institute of Control and Industrial Electronics, Warsaw University of Technology, Warsaw (Poland)

    1997-12-31

    This paper presents a new control strategy in the discrete Direct Torque Control (DTC) based on neuro-fuzzy structure. Two schemes are proposed: neuro-fuzzy switching times calculator and neuro-fuzzy incremental controller with space vector modulator. These control strategies guarantee very good dynamic and steady-states characteristics, with very low sampling time and constant switching frequency. The proposed techniques are verified by simulation study of the whole drive system and results are compared with conventional discrete Direct Torque Control method. (orig.) 18 refs.

  5. Neuro fuzzy force control for soft dry contact Hertzian ultrasonic probe

    Science.gov (United States)

    Gallegos, E.; Baltazar, A.; Treesatayapun, C.

    2016-02-01

    In this work the use of a cartesian robotic manipulator as scanner for the automated identification of hidden defects in an aluminum test plate is proposed. The robotic manipulator includes a custom made soft deformable ultrasonic probe and a force sensor for the recollection of the ultrasonic signals and force feedback. The contact between the soft probe and the test plate is regulated using a Neuro Fuzzy controller in order to avoid the complex mathematical model produced by the interaction. Finally the use of the correlation coefficient is proposed for the post processing of the obtained ultrasonic signals and identification of hidden defects inside the test plate. Experimental studies demonstrated the efficiency of the method.

  6. Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach.

    Science.gov (United States)

    Ahmed, Sk Saddam; Dey, Nilanjan; Ashour, Amira S; Sifaki-Pistolla, Dimitra; Bălas-Timar, Dana; Balas, Valentina E; Tavares, João Manuel R S

    2017-01-01

    Crohn's disease (CD) diagnosis is a tremendously serious health problem due to its ultimately effect on the gastrointestinal tract that leads to the need of complex medical assistance. In this study, the backpropagation neural network fuzzy classifier and a neuro-fuzzy model are combined for diagnosing the CD. Factor analysis is used for data dimension reduction. The effect on the system performance has been investigated when using fuzzy partitioning and dimension reduction. Additionally, further comparison is done between the different levels of the fuzzy partition to reach the optimal performance accuracy level. The performance evaluation of the proposed system is estimated using the classification accuracy and other metrics. The experimental results revealed that the classification with level-8 partitioning provides a classification accuracy of 97.67 %, with a sensitivity and specificity of 96.07 and 100 %, respectively.

  7. Forecasting of the development of professional medical equipment engineering based on neuro-fuzzy algorithms

    Science.gov (United States)

    Vaganova, E. V.; Syryamkin, M. V.

    2015-11-01

    The purpose of the research is the development of evolutionary algorithms for assessments of promising scientific directions. The main attention of the present study is paid to the evaluation of the foresight possibilities for identification of technological peaks and emerging technologies in professional medical equipment engineering in Russia and worldwide on the basis of intellectual property items and neural network modeling. An automated information system consisting of modules implementing various classification methods for accuracy of the forecast improvement and the algorithm of construction of neuro-fuzzy decision tree have been developed. According to the study result, modern trends in this field will focus on personalized smart devices, telemedicine, bio monitoring, «e-Health» and «m-Health» technologies.

  8. Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources

    Directory of Open Access Journals (Sweden)

    Otilia Elena Dragomir

    2015-11-01

    Full Text Available The challenge for our paper consists in controlling the performance of the future state of a microgrid with energy produced from renewable energy sources. The added value of this proposal consists in identifying the most used criteria, related to each modeling step, able to lead us to an optimal neural network forecasting tool. In order to underline the effects of users’ decision making on the forecasting performance, in the second part of the article, two Adaptive Neuro-Fuzzy Inference System (ANFIS models are tested and evaluated. Several scenarios are built by changing: the prediction time horizon (Scenario 1 and the shape of membership functions (Scenario 2.

  9. A Neuro-Fuzzy System for Characterization of Arm Movements

    Directory of Open Access Journals (Sweden)

    Alexandre Balbinot

    2013-02-01

    Full Text Available The myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to also include applications in diverse areas such as rehabilitation, movement analysis and myoelectric control of prosthesis. This paper aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-fuzzy, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement. The average accuracy obtained was 86% to 7 distinct movements in tests of long duration (about three hours.

  10. Extraction of rules for faulty bearing classification by a Neuro-Fuzzy approach

    Science.gov (United States)

    Marichal, G. N.; Artés, Mariano; García Prada, J. C.; Casanova, O.

    2011-08-01

    In this paper, a classification system of faulty bearings based on a Neuro-Fuzzy approach is presented. The vibration signals in the frequency domain produced by the faulty bearings will be taken as the inputs to the classification system. In this sense, it is an essential characteristic for the used Neuro-Fuzzy approach, the possibility of taking a great number of inputs. The system consists of several Neuro-Fuzzy systems for determining different bearing status, along with a measurement equipment of the vibration spectral data. In this paper, a special attention is focused on the analysis of the rules obtained by the final Neuro-Fuzzy system. In fact, a rule extraction process and an interpretation rule process is discussed. Several trials have been carried out, taking into account the vibration spectral data collected by the measurement equipment, where satisfactory results have been achieved.

  11. A Combined Methodology of Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm for Short-term Energy Forecasting

    Directory of Open Access Journals (Sweden)

    KAMPOUROPOULOS, K.

    2014-02-01

    Full Text Available This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS and Genetic Algorithms (GA. The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a short-term load forecasting for the different modeled consumption processes.

  12. Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

    Science.gov (United States)

    Akhavan, P.; Karimi, M.; Pahlavani, P.

    2014-10-01

    Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

  13. Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    P. Akhavan

    2014-10-01

    Full Text Available Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

  14. Short-term and long-term thermal prediction of a walking beam furnace using neuro-fuzzy techniques

    Directory of Open Access Journals (Sweden)

    Banadaki Hamed Dehghan

    2015-01-01

    Full Text Available The walking beam furnace (WBF is one of the most prominent process plants often met in an alloy steel production factory and characterized by high non-linearity, strong coupling, time delay, large time-constant and time variation in its parameter set and structure. From another viewpoint, the WBF is a distributed-parameter process in which the distribution of temperature is not uniform. Hence, this process plant has complicated non-linear dynamic equations that have not worked out yet. In this paper, we propose one-step non-linear predictive model for a real WBF using non-linear black-box sub-system identification based on locally linear neuro-fuzzy (LLNF model. Furthermore, a multi-step predictive model with a precise long prediction horizon (i.e., ninety seconds ahead, developed with application of the sequential one-step predictive models, is also presented for the first time. The locally linear model tree (LOLIMOT which is a progressive tree-based algorithm trains these models. Comparing the performance of the one-step LLNF predictive models with their associated models obtained through least squares error (LSE solution proves that all operating zones of the WBF are of non-linear sub-systems. The recorded data from Iran Alloy Steel factory is utilized for identification and evaluation of the proposed neuro-fuzzy predictive models of the WBF process.

  15. Adaptive neuro-fuzzy estimation of optimal lens system parameters

    Science.gov (United States)

    Petković, Dalibor; Pavlović, Nenad T.; Shamshirband, Shahaboddin; Mat Kiah, Miss Laiha; Badrul Anuar, Nor; Idna Idris, Mohd Yamani

    2014-04-01

    Due to the popularization of digital technology, the demand for high-quality digital products has become critical. The quantitative assessment of image quality is an important consideration in any type of imaging system. Therefore, developing a design that combines the requirements of good image quality is desirable. Lens system design represents a crucial factor for good image quality. Optimization procedure is the main part of the lens system design methodology. Lens system optimization is a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. Therefore lens system design provides ideal problems for intelligent optimization algorithms. There are many tools which can be used to measure optical performance. One very useful tool is the spot diagram. The spot diagram gives an indication of the image of a point object. In this paper, one optimization criterion for lens system, the spot size radius, is considered. This paper presents new lens optimization methods based on adaptive neuro-fuzzy inference strategy (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.

  16. Adaptive Neuro-Fuzzy Technique for Autonomous Ground Vehicle Navigation

    Directory of Open Access Journals (Sweden)

    Auday Al-Mayyahi

    2014-11-01

    Full Text Available This article proposes an adaptive neuro-fuzzy inference system (ANFIS for solving navigation problems of an autonomous ground vehicle (AGV. The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD; right distance (RD and left distance (LD for the low-level motion control. Two heading controllers deploy the angle difference (AD between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles.

  17. 用多元矢量自回归模型推断微生物相互作用关系%Inference of microbial interactions using multivariate vector auto-regression model

    Institute of Scientific and Technical Information of China (English)

    刘飞

    2014-01-01

    识别微生物相互作用关系对理解微生物社团的结构和功能非常重要,一般的推断微生物相互作用关系的计算方法都是基于微生物个体相似性来提出的。比起来自多个不同社区的相互作用网络,一个复杂社区的时间动态性可以揭示更为复杂的相互作用关系。尽管已经提出了很多相似性方法来分析时间序列数据,但是没有有效的多元统计方法来推断和评估作用关系的统计显著性。在本文中,我们提出从人类肠道微生物的时间序列数据来推断出微生物动态相互作用,我们使用多元统计方法--矢量自回归(MVAR)模型,并应用它对重复抗生素扰动的人类肠道微生物时间序列数据集进行网络预测。所涉及的微生物相互作用提供了一个微生物社团的动态观点,这可能是对相似或相关方法的一种新型补充。%There is an increasing interest in identifying the microbial interactions that are important to under-stand the structure and the function of microbial community. Computational inference methods of microbial relation-ships are currently based on the similarity among microbial individuals. The dynamics of a single complex communi-ty over time can reveal complex interacting patterns than collecting samples from multiple distinct communities. Al-though similarity-based method has been proposed for analyzing time series data, there are no efficient multivariate statistical methods to infer and access the statistical significance of the estimated associations. In this paper, we pro-vide the first attempt to infer dynamic microbial interactions from the time series of human gut microbiomes. We use a multivariate statistical method-Vector Auto-regression (MVAR) model and apply the method on time series datas-et of human gut microbiomes with repeated antibiotic perturbations. The referred microbial interactions provide a dy-namical view of a microbial community which could

  18. Comments on ‘A comparative study of ANN and neuro-fuzzy for the prediction of dynamic constant of rockmass’ by T N Singh, R Kanchan, A K Verma and K Saigal

    Indian Academy of Sciences (India)

    Tarkan Erdik; Zekai Şen

    2008-12-01

    Singh et al (2005)examined the potential of the ANN and neuro-fuzzy systems application for the prediction of dynamic constant of rockmass. However,the model proposed by them has some drawbacks according to fuzzy logic principles.This discussion will focus on the main fuzzy logic principles which authors and potential readers should take into consideration.

  19. SARIMA模型与残差自回归模型在甲肝发病率预测中的应用及比较%Application and comparison of SARIMA and Auto-Regressive model in predicting incidence of hepatitis A

    Institute of Scientific and Technical Information of China (English)

    张兴裕; 周丽君; 刘元元; 李晓松

    2012-01-01

    目的 比较季节性求和自回归移动平均(SARIMA)模型及残差自回归模型在甲肝发病率预测中的应用效果.方法 根据四川省2004年1月~2009年6月的甲肝月发病率资料,分别拟合SARIMA模型和残差自回归模型,比较两种 模型的拟合及预测效果.结果 SARIMA模型的AIC值和BIC值分别为64.98和59.07,残差自回归模型的AIC值和BIC值分别为110.01和103.44; SARIMA模型的拟合值与实际值的MAPE、MAE及MSE分别为0.034 9、0.083 5及0.001 6,残差自回归模型的SARIMA的拟合值与实际值的MAPE、MAE及MSE分别为0.055 7、0.139 2及0.005 0.结论SARIMA模型的拟合与预测效果优于残差自回归模型.%OBJECTIVE To compare the efficiency of SARIMA model and Auto-Regressive model in the application of predicting the incidence of seasonal infectious disease. METHODS Monthly incidences data of hepatitis A in Sichuan province from Jan.2004 to Jun.2009 were used to fit SARIMA model and Auto-Regressive model respectively. The incidences of the last six months of 2009 were regarded as the testing data so as to compare the fitting and predictive efficiency of the two models. RESULTS AIC and BIC of SAIMA model were 64.9 and 59.07; AIC and BIC of Auto-Regressive model were 110.01 and 103.44. The MAPE, MAE and MSE of the predicted values by SARIMA were 0.034 9, 0.083 5 and 0.001 6, respectively; the MAPE, MAE and MSE of the predicted values by SARIMA were 0.055 7, 0.139 2 and 0.005 0, respectively. CONCLUSION The fitting and predicting effects of SARIMA are better than Auto-Regressive model.

  20. Predicting Packet Transmission Data over IP Networks Using Adaptive Neuro-Fuzzy Inference Systems

    Directory of Open Access Journals (Sweden)

    Samira Chabaa

    2009-01-01

    Full Text Available Problem statement: The statistical modeling for predicting network traffic has now become a major tool used for network and is of significant interest in many domains: Adaptive application, congestion and admission control, wireless, network management and network anomalies. To comprehend the properties of IP-network traffic and system conditions, many kinds of reports based on measured network traffic data have been reported by several researchers. The goal of the present contribution was to complement these previous researches by predicting network traffic data. Approach: The Adaptive Neuro-Fuzzy Inference System (ANFIS was realized by an appropriate combination of fuzzy systems and neural networks. It was applied in different applications which have been increased in recent years and have multidisciplinary in several domains with a high accuracy. For this reason, we used a set of input and output data of packet transmission over Internet Protocol (IP networks as input and output of ANFIS to develop a model for predicting data. Results: ANFIS was compared with some existing model based on Volterra system with Laguerre functions. The obtained results demonstrate that the sequences of generated values have the same statistical characteristics as those really observed. Furthermore, the relative error using ANFIS model was better than this obtained by Volterra system model. Conclusion: The developed model fits well real data and can be used for predicting purpose with a high accuracy.

  1. Efficient neuro-fuzzy system and its Memristor Crossbar-based Hardware Implementation

    CERN Document Server

    Merrikh-Bayat, Farnood

    2011-01-01

    In this paper a novel neuro-fuzzy system is proposed where its learning is based on the creation of fuzzy relations by using new implication method without utilizing any exact mathematical techniques. Then, a simple memristor crossbar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting properties. In addition to high connectivity between neurons and being fault-tolerant, all synaptic weights in our proposed method are always non-negative and there is no need to precisely adjust them. Finally, this structure is hierarchically expandable and can compute operations in real time since it is implemented through analog circuits. Simulation results show the efficiency and applicability of our neuro-fuzzy computing system. They also indicate that this system can be a good candidate to be used for creating artificial brain.

  2. A Neuro-Fuzzy Approach in the Classification of Students’ Academic Performance

    Directory of Open Access Journals (Sweden)

    Quang Hung Do

    2013-01-01

    Full Text Available Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions.

  3. Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.

    Science.gov (United States)

    Dzakpasu, Mawuli; Scholz, Miklas; McCarthy, Valerie; Jordan, Siobhán; Sani, Abdulkadir

    2015-01-01

    Monitoring large-scale treatment wetlands is costly and time-consuming, but required by regulators. Some analytical results are available only after 5 days or even longer. Thus, adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the effluent concentrations of 5-day biochemical oxygen demand (BOD5) and NH4-N from a full-scale integrated constructed wetland (ICW) treating domestic wastewater. The ANFIS models were developed and validated with a 4-year data set from the ICW system. Cost-effective, quicker and easier to measure variables were selected as the possible predictors based on their goodness of correlation with the outputs. A self-organizing neural network was applied to extract the most relevant input variables from all the possible input variables. Fuzzy subtractive clustering was used to identify the architecture of the ANFIS models and to optimize fuzzy rules, overall, improving the network performance. According to the findings, ANFIS could predict the effluent quality variation quite strongly. Effluent BOD5 and NH4-N concentrations were predicted relatively accurately by other effluent water quality parameters, which can be measured within a few hours. The simulated effluent BOD5 and NH4-N concentrations well fitted the measured concentrations, which was also supported by relatively low mean squared error. Thus, ANFIS can be useful for real-time monitoring and control of ICW systems.

  4. A Synergistic Effect in the Measurement of Neuro-Fuzzy System

    Directory of Open Access Journals (Sweden)

    Gorbachev Sergey

    2016-01-01

    Full Text Available We consider a new type of hybrid neuro-fuzzy system based on fuzzy and neural computing in hierarchical sequential structure, the total effect exceeds the effect of each component separately. The proposed system can be applied to multi-criteria analysis, automatic classification on signs and obtain evidence-based estimates of the efficiency of scientific and technical solutions and technologies, engineering and robotics. An example of a neuro-fuzzy system measuring the intensity of the emotions of a robot, with the extraction of diagnostic decision rules “If & then”.

  5. Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems.

    Science.gov (United States)

    Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A

    2012-01-01

    Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.

  6. Development of Neuro-fuzzy System for Early Prediction of Heart Attack

    Directory of Open Access Journals (Sweden)

    Obanijesu Opeyemi

    2012-08-01

    Full Text Available This work is aimed at providing a neuro-fuzzy system for heart attack detection. Theneuro-fuzzy system was designed with eight input field and one output field. The input variables are heart rate, exercise, blood pressure, age, cholesterol, chest pain type, blood sugar and sex. The output detects the risk levels of patients which are classified into 4 different fields: very low, low, high and very high. The data set used was extracted from the database and modeled in order to make it appropriate for the training, then the initial FIS structure was generated, the network was trained with the set of training data after which it was tested and validated with the set of testing data. The output of the system was designed in a way that the patient can use it personally. The patient just need to supply some values which serve as input to the system and based on the values supplied the system will be able to predict the risk level of the patient.

  7. Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE

    Directory of Open Access Journals (Sweden)

    Aderemi A Atayero

    2012-04-01

    Full Text Available ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index are used to adjust hysteresis task of load balancing.

  8. Adaptive Neuro Fuzzy Inference Controller for Full Vehicle Nonlinear Active Suspension Systems

    Directory of Open Access Journals (Sweden)

    A. Aldair

    2010-12-01

    Full Text Available The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PIλ Dμ (FOPID controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.

  9. Prediksi Penjualan Barang Menggunakan Metode Adaptive Neuro-Fuzzy Inference System (ANFIS

    Directory of Open Access Journals (Sweden)

    Allyna Virrayyani

    2016-12-01

    Full Text Available Prediksi penjualan barang merupakan salah satu cara untuk menjaga stabilitas penjualan barang. Hasil prediksi yang diperoleh dapat dijadikan sebagai pertimbangan untuk mengambil keputusan dalam perencanaan manajemen bisnis. Salah satu metode yang dapat digunakan untuk prediksi adalah Adaptive Neuro-Fuzzy Inference System (ANFIS. Di dalam penelitian ini, ANFIS diimplementasikan dalam sebuah aplikasi sistem prediksi penjualan barang. Prosedur prediksi menggunakan analisis runtun waktu. Aturan ANFIS menggunakan model fuzzy Takagi-Sugeno dan fungsi keanggotaan tipe Generalized bell dengan 2 data masukan untuk 1 data target. Dari hasil pelatihan dan pengujian ANFIS untuk penjualan Beras Delanggu Raja, diperoleh nilai Mean Absolute Persentage (MAPE pelatihan sebesar 9.4180332828% dan diperoleh nilai MAPE pengujian sebesar 7.5343642644%. Hasil MAPE pengujian tersebut kurang dari batas toleransi error, yaitu 20 %. Batas toleransi tersebut berdasarkan penafsiran Batey dan Friedrich di mana MAPE < 10% merupakan perkiraan yang sangat baik dan 10% < MAPE < 20% merupakan perkiraan yang baik. ANFIS berhasil memprediksi penjualan Beras Delanggu Raja pada bulan yang akan datang dengan total 4944. Aplikasi sistem telah diuji menggunakan pengujian black-box. Seluruh prosedur pengujian dinyatakan berhasil.

  10. Implementation of Computer Vision Based Industrial Fire Safety Automation by Using Neuro-Fuzzy Algorithms

    Directory of Open Access Journals (Sweden)

    Manjunatha K.C.

    2015-03-01

    Full Text Available A computer vision-based automated fire detection and suppression system for manufacturing industries is presented in this paper. Automated fire suppression system plays a very significant role in Onsite Emergency System (OES as it can prevent accidents and losses to the industry. A rule based generic collective model for fire pixel classification is proposed for a single camera with multiple fire suppression chemical control valves. Neuro-Fuzzy algorithm is used to identify the exact location of fire pixels in the image frame. Again the fuzzy logic is proposed to identify the valve to be controlled based on the area of the fire and intensity values of the fire pixels. The fuzzy output is given to supervisory control and data acquisition (SCADA system to generate suitable analog values for the control valve operation based on fire characteristics. Results with both fire identification and suppression systems have been presented. The proposed method achieves up to 99% of accuracy in fire detection and automated suppression.

  11. An Adaptive Neuro-Fuzzy Inference System for Sea Level Prediction Considering Tide-Generating Forces and Oceanic Thermal Expansion

    Directory of Open Access Journals (Sweden)

    Li-Ching Lin Hsien-Kuo Chang

    2008-01-01

    Full Text Available The paper presents an adaptive neuro fuzzy inference system for predicting sea level considering tide-generating forces and oceanic thermal expansion assuming a model of sea level dependence on sea surface temperature. The proposed model named TGFT-FN (Tide-Generating Forces considering sea surface Temperature and Fuzzy Neuro-network system is applied to predict tides at five tide gauge sites located in Taiwan and has the root mean square of error of about 7.3 - 15.0 cm. The capability of TGFT-FN model is superior in sea level prediction than the previous TGF-NN model developed by Chang and Lin (2006 that considers the tide-generating forces only. The TGFT-FN model is employed to train and predict the sea level of Hua-Lien station, and is also appropriate for the same prediction at the tide gauge sites next to Hua-Lien station.

  12. A comparative study of ANN and Neuro-fuzzy for the prediction of dynamic constant of rockmass

    Indian Academy of Sciences (India)

    T N Singh; R Kanchan; A K Verma; K Saigal

    2005-02-01

    Physico-mechanical properties of rocks have great significance in all operational parts in mining activities, from exploration to final dispatch of material. Compressional wave velocity (-wave velocity) and anisotropic behaviour of rocks are two such properties which help to understand the rock response under varying stress conditions. They also influence the breakage mechanism of rock. There are different methods to determine the -wave velocity and anisotropy in situ and in the laboratory. These methods are cumbersome and time consuming. Fuzzy set theory, Fuzzy logic and Neural Networks techniques seem very well suited for typical geotechnical problems. In conjunction with statistics and conventional mathematical methods, hybrid methods can be developed that may prove to be a step forward in modeling geotechnical problems. Here, we have developed and compared two di®erent models, Neuro-fuzzy systems (combination of fuzzy and artificial neural network systems) and Artificial neural network systems, for the prediction of compressional wave velocity.

  13. Neuro-fuzzy GMDH based particle swarm optimization for prediction of scour depth at downstream of grade control structures

    Directory of Open Access Journals (Sweden)

    Mohammad Najafzadeh

    2015-03-01

    Full Text Available In the present study, neuro-fuzzy based-group method of data handling (NF-GMDH as an adaptive learning network was utilized to predict the maximum scour depth at the downstream of grade-control structures. The NF-GMDH network was developed using particle swarm optimization (PSO. Effective parameters on the scour depth include sediment size, geometry of weir, and flow characteristics in the upstream and downstream of structure. Training and testing of performances were carried out using non-dimensional variables. Datasets were divided into three series of dataset (DS. The testing results of performances were compared with the gene-expression programming (GEP, evolutionary polynomial regression (EPR model, and conventional techniques. The NF-GMDH-PSO network produced lower error of the scour depth prediction than those obtained using the other models. Also, the effective input parameter on the maximum scour depth was determined through a sensitivity analysis.

  14. Statistical early-warning indicators based on Auto-Regressive Moving-Average processes

    CERN Document Server

    Faranda, Davide; Dubrulle, Bérengère

    2014-01-01

    We address the problem of defining early warning indicators of critical transition. To this purpose, we fit the relevant time series through a class of linear models, known as Auto-Regressive Moving-Average (ARMA(p,q)) models. We define two indicators representing the total order and the total persistence of the process, linked, respectively, to the shape and to the characteristic decay time of the autocorrelation function of the process. We successfully test the method to detect transitions in a Langevin model and a 2D Ising model with nearest-neighbour interaction. We then apply the method to complex systems, namely for dynamo thresholds and financial crisis detection.

  15. Anti-Swing and Position Control of Double Inverted Pendulum (DIP on Cart Using Hybrid Neuro-Fuzzy Controllers

    Directory of Open Access Journals (Sweden)

    Ashwani Kharola

    2016-07-01

    Full Text Available This paper illustrates a comparison study for control of highly non-linear Double Inverted Pendulum (DIP on cart. A Matlab-Simulink model of DIP has been built using Newton's second law. The Neuro-fuzzy controllers stabilizes pendulums at vertical position while cart moves in horizontal direction. This study proposes two soft-computing techniques namely Fuzzy logic reasoning and Neural networks (NN's for control of DIP systems. The results shows that Fuzzy controllers provides better results as compared to NN's controllers in terms of settling time (sec, maximum overshoot (degree and steady state error. The regression (R and mean square error (MSE values obtained after training of Neural network were satisfactory. The simulation results proves the validity of proposed techniques.

  16. Neuro-fuzzy estimation of passive robotic joint safe velocity with embedded sensors of conductive silicone rubber

    Science.gov (United States)

    Al-Shammari, Eiman Tamah; Petković, Dalibor; Danesh, Amir Seyed; Shamshirband, Shahaboddin; Issa, Mirna; Zentner, Lena

    2016-05-01

    Robotic operations need to be safe for unpredictable contacts. Joints with passive compliance with springs can be used for soft robotic contacts. However the joints cannot measure external collision forces. In this investigation was developed one passive compliant joint which have soft contacts with external objects and measurement capabilities. To ensure it, conductive silicone rubber was used as material for modeling of the compliant segments of the robotic joint. These compliant segments represent embedded sensors. The conductive silicone rubber is electrically conductive by deformations. The main task was to obtain elastic absorbers for the external collision forces. These absorbers can be used for measurement in the same time. In other words, the joint has an internal measurement system. Adaptive neuro fuzzy inference system (ANFIS) was used to estimate the safety level of the robotic joint by head injury criteria (HIC).

  17. Indirect adaptive control of nonlinear systems based on bilinear neuro-fuzzy approximation.

    Science.gov (United States)

    Boutalis, Yiannis; Christodoulou, Manolis; Theodoridis, Dimitrios

    2013-10-01

    In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not

  18. Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

    Science.gov (United States)

    Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Lee, Siew Cheng

    2016-05-01

    Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (e̅a), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.

  19. Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Boumediene ALLAOUA

    2009-12-01

    Full Text Available This paper presents an application of Adaptive Neuro-Fuzzy Inference System (ANFIS control for DC motor speed optimized with swarm collective intelligence. First, the controller is designed according to Fuzzy rules such that the systems are fundamentally robust. Secondly, an adaptive Neuro-Fuzzy controller of the DC motor speed is then designed and simulated; the ANFIS has the advantage of expert knowledge of the Fuzzy inference system and the learning capability of neural networks. Finally, the ANFIS is optimized by Swarm Intelligence. Digital simulation results demonstrate that the deigned ANFIS-Swarm speed controller realize a good dynamic behavior of the DC motor, a perfect speed tracking with no overshoot, give better performance and high robustness than those obtained by the ANFIS alone.

  20. ON THE DESIGN OF A NEURO-FUZZY CONTROLLER - APPLICATION TO THE CONTROL OF A BIOREACTOR

    Institute of Scientific and Technical Information of China (English)

    Joseph HAGGEGE; Mohamed BENREJEB; Pierre BORNE

    2005-01-01

    This paper presents a new methodological approach for the synthesis of a neuro-fuzzy controller,using an on-line learning procedure. A simple algebraic formulation of a Sugeno fuzzy inference system that ensures a coherent universe of discourse, making easy its interpretation by a human being,is proposed and implemented in the case of the control of a bioreactor, which is considered as a complex non linear process.

  1. Design of Synthetic Optimizing Neuro Fuzzy Temperature Controller for Dual Screw Profile Plastic Extruder Using Labview

    Directory of Open Access Journals (Sweden)

    Ravi Samikannu

    2011-01-01

    Full Text Available Problem statement: The temperature control in plastic extrusion machine is an important factor to produce high quality plastic products. The first order temperature control system in plastic extrusion comprises of coupling effects, long delay time and large time constants. Controlling temperature is very difficult as the process is multistage process and the system coupled with each other. In order to conquer this problem the system is premeditated with neuro fuzzy controller using LabVIEW. Approach: The existing technique involved is conventional PID controller, Neural controller, mamdani type Fuzzy Logic Controller and the proposed method is neuro fuzzy controller. Results: Manifest feature of the proposed method is smoothing of undesired control signal of conventional PID, neural controller and mamdani type FLC controller. The software incorporated the LabVIEW graphical programming language and MATLAB toolbox were used to design temperature control in plastic extrusion system. Hence neuro fuzzy controller is most powerful approach to retrieve the adaptiveness in the case of nonlinear system. Conclusion: The tuning of the controller was synchronized with the controlled variable and allowing the process at its desired operating condition. The results indicated that the use of proposed controller improve the process in terms of time domain specification, set point tracking and also reject disturbances with optimum stability.

  2. Applied to neuro-fuzzy models for signal validation in Angra 1 nuclear power plant; Modelos de validacao de sinal utilizando tecnicas de inteligencia artificial aplicados a um reator nuclear

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Mauro Vitor de

    1999-06-15

    This work develops two models of signal validation in which the analytical redundancy of the monitored signals from an industrial plant is made by neural networks. In one model the analytical redundancy is made by only one neural network while in the other it is done by several neural networks, each one working in a specific part of the entire operation region of the plant. Four cluster techniques were tested to separate the entire region of operation in several specific regions. An additional information of systems' reliability is supplied by a fuzzy inference system. The models were implemented in C language and tested with signals acquired from Angra I nuclear power plant, from its start to 100% of power. (author)

  3. Using adaptive neuro-fuzzy inference system technique for crosstalk correction in simultaneous {sup 99m}Tc/{sup 201}Tl SPECT imaging: A Monte Carlo simulation study

    Energy Technology Data Exchange (ETDEWEB)

    Heidary, Saeed, E-mail: saeedheidary@aut.ac.ir; Setayeshi, Saeed, E-mail: setayesh@aut.ac.ir

    2015-01-11

    This work presents a simulation based study by Monte Carlo which uses two adaptive neuro-fuzzy inference systems (ANFIS) for cross talk compensation of simultaneous {sup 99m}Tc/{sup 201}Tl dual-radioisotope SPECT imaging. We have compared two neuro-fuzzy systems based on fuzzy c-means (FCM) and subtractive (SUB) clustering. Our approach incorporates eight energy-windows image acquisition from 28 keV to 156 keV and two main photo peaks of {sup 201}Tl (77±10% keV) and {sup 99m}Tc (140±10% keV). The Geant4 application in emission tomography (GATE) is used as a Monte Carlo simulator for three cylindrical and a NURBS Based Cardiac Torso (NCAT) phantom study. Three separate acquisitions including two single-isotopes and one dual isotope were performed in this study. Cross talk and scatter corrected projections are reconstructed by an iterative ordered subsets expectation maximization (OSEM) algorithm which models the non-uniform attenuation in the projection/back-projection. ANFIS-FCM/SUB structures are tuned to create three to sixteen fuzzy rules for modeling the photon cross-talk of the two radioisotopes. Applying seven to nine fuzzy rules leads to a total improvement of the contrast and the bias comparatively. It is found that there is an out performance for the ANFIS-FCM due to its acceleration and accurate results.

  4. Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques

    OpenAIRE

    Abraham, Ajith

    2004-01-01

    Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantages of a combination of ANN a...

  5. Detailed comparison of neuro-fuzzy estimation of subpixel land-cover composition from remotely sensed data

    Science.gov (United States)

    Baraldi, Andrea; Binaghi, Elisabetta; Blonda, Palma N.; Brivio, Pietro A.; Rampini, Anna

    1998-10-01

    Mixed pixels, which do not follow a known statistical distribution that could be parameterized, are a major source of inconvenience in classification of remote sensing images. This paper reports on an experimental study designed for the in-depth investigation of how and why two neuro-fuzzy classification schemes, whose properties are complementary, estimate sub-pixel land cover composition from remotely sensed data. The first classifier is based on the fuzzy multilayer perceptron proposed by Pal and Mitra: the second classifier consists of a two-stage hybrid (TSH) learning scheme whose unsupervised first stage is based on the fully self- organizing simplified adaptive resonance theory clustering network proposed by Baraldi. Results of the two neuro-fuzzy classifiers are assessed by means of specific evaluation tools designed to extend conventional descriptive and analytical statistical estimators to the case of multi-membership in classes. When a synthetic data set consisting of pure and mixed pixels is processed by the two neuro-fuzzy classifiers, experimental result show that: i) the two neuro- fuzzy classifiers perform better than the traditional MLP; ii) classification accuracies of the two neuro-fuzzy classifiers are comparable; and iii) the TSH classifier requires to train less background knowledge than FMLP.

  6. Prediction of Radical Scavenging Activities of Anthocyanins Applying Adaptive Neuro-Fuzzy Inference System (ANFIS with Quantum Chemical Descriptors

    Directory of Open Access Journals (Sweden)

    Changho Jhin

    2014-08-01

    Full Text Available Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A and electronegativity (χ of flavylium cation, and ionization potential (I of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively.

  7. Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network

    Science.gov (United States)

    Dewan, Mohammad W.; Huggett, Daniel J.; Liao, T. Warren; Wahab, Muhammad A.; Okeil, Ayman M.

    2015-01-01

    Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (??), plunge force (????), and welding speed (??) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (??,??,????,??????) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.

  8. Using Adaptive Neuro-Fuzzy Inference System in Alert Management of Intrusion Detection Systems

    Directory of Open Access Journals (Sweden)

    Zahra Atashbar Orang

    2012-10-01

    Full Text Available By ever increase in using computer network and internet, using Intrusion Detection Systems (IDS has been more important. Main problems of IDS are the number of generated alerts, alert failure as well as identifying the attack type of alerts. In this paper a system is proposed that uses Adaptive Neuro-Fuzzy Inference System to classify IDS alerts reducing false positive alerts and also identifying attack types of true positive ones. By the experimental results on DARPA KDD cup 98, the system can classify alerts, leading a reduction of false positive alerts considerably and identifying attack types of alerts in low slice of time.

  9. A Neuro-Fuzzy based System for Classification of Natural Textures

    Science.gov (United States)

    Jiji, G. Wiselin

    2016-12-01

    A statistical approach based on the coordinated clusters representation of images is used for classification and recognition of textured images. In this paper, two issues are being addressed; one is the extraction of texture features from the fuzzy texture spectrum in the chromatic and achromatic domains from each colour component histogram of natural texture images and the second issue is the concept of a fusion of multiple classifiers. The implementation of an advanced neuro-fuzzy learning scheme has been also adopted in this paper. The results of classification tests show the high performance of the proposed method that may have industrial application for texture classification, when compared with other works.

  10. Comparative Evaluation of Adaptive Filter and Neuro-Fuzzy Filter in Artifacts Removal From Electroencephalogram Signal

    Directory of Open Access Journals (Sweden)

    Paulchamy Balaiah

    2012-01-01

    Full Text Available Problem statement: This study presents an effective method for removing mixed artifacts (EOG-Electro-ocular gram, ECG-Electrocardiogram, EMG-Electromyogram from the EEG-Electroencephalogram records. The noise sources increases the difficulty in analyzing the EEG and obtaining clinical information. EEG signals are multidimensional, non-stationary (i.e., statistical properties are not invariant in time, time domain biological signals, which are not reproducible. It is supposed to contain information about what is going on in the ensemble of excitatory pyramidal neuron level, at millisecond temporal resolution scale. Since scalp EEG contains considerable amount of noise and artifacts and exactly where it is coming from is poorly determined, extracting information from it is extremely challenging. For this reason it is necessary to design specific filters to decrease such artifacts in EEG records. Approach: Some of the other methods that are really appealing are artifact removal through Independent Component Analysis (ICA, Wavelet Transforms, Linear filtering and Artificial Neural Networks. ICA method could be used in situations, where large numbers of noises need to be distinguished, but it is not suitable for on-line real time application like Brain Computer Interface (BCI. Wavelet transforms are suitable for real-time application, but there all success lies in the selection of the threshold function. Linear filtering is best when; the frequency of noises does not interfere or overlap with each other. In this study we proposed adaptive filtering and neuro-fuzzy filtering method to remove artifacts from EEG. Adaptive filter performs linear filtering. Neuro-fuzzy approaches are very promising for non-linear filtering of noisy image. The multiple-output structure is based on recursive processing. It is able to adapt the filtering action to different kinds of corrupting noise. Fuzzy reasoning embedded into the network structure aims at reducing errors

  11. Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements

    Directory of Open Access Journals (Sweden)

    Mosbeh R. Kaloop

    2015-10-01

    Full Text Available This study describes the performance assessment of the Huangpu Bridge in Guangzhou, China based on long-term monitoring in real-time by the kinematic global positioning system (RTK-GPS technique. Wavelet transformde-noising is applied to filter the GPS measurements, while the adaptive neuro-fuzzy inference system (ANFIS time series output-only model is used to predict the deformations of GPS-bridge monitoring points. In addition, GPS and accelerometer monitoring systems are used to evaluate the bridge oscillation performance. The conclusions drawn from investigating the numerical results show that: (1the wavelet de-noising of the GPS measurements of the different recording points on the bridge is a suitable tool to efficiently eliminate the signal noise and extract the different deformation components such as: semi-static and dynamic displacements; (2 the ANFIS method with two multi-input single output model is revealed to powerfully predict GPS movement measurements and assess the bridge deformations; and (3 The installed structural health monitoring system and the applied ANFIS movement prediction performance model are solely sufficient to assure bridge safety based on the analyses of the different filtered movement components.

  12. Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Metin Ertunc, H. [Department of Mechatronics Engineering, Kocaeli University, Umuttepe, 41380 Kocaeli (Turkey); Hosoz, Murat [Department of Mechanical Education, Kocaeli University, Umuttepe, 41380 Kocaeli (Turkey)

    2008-12-15

    This study deals with predicting the performance of an evaporative condenser using both artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques. For this aim, an experimental evaporative condenser consisting of a copper tube condensing coil along with air and water circuit elements was developed and equipped with instruments used for temperature, pressure and flow rate measurements. After the condenser was connected to an R134a vapour-compression refrigeration circuit, it was operated at steady state conditions, while varying both dry and wet bulb temperatures of the air stream entering the condenser, air and water flow rates as well as pressure, temperature and flow rate of the entering refrigerant. Using some of the experimental data for training, ANN and ANFIS models for the evaporative condenser were developed. These models were used for predicting the condenser heat rejection rate, refrigerant temperature leaving the condenser along with dry and wet bulb temperatures of the leaving air stream. Although it was observed that both ANN and ANFIS models yielded a good statistical prediction performance in terms of correlation coefficient, mean relative error, root mean square error and absolute fraction of variance, the accuracies of ANFIS predictions were usually slightly better than those of ANN predictions. This study reveals that, having an extended prediction capability compared to ANN, the ANFIS technique can also be used for predicting the performance of evaporative condensers. (author)

  13. Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing electro chemical machining process

    Science.gov (United States)

    Teimouri, Reza; Sohrabpoor, Hamed

    2013-12-01

    Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.

  14. Prediction of matching condition for a microstrip subsystem using artificial neural network and adaptive neuro-fuzzy inference system

    Science.gov (United States)

    Salehi, Mohammad Reza; Noori, Leila; Abiri, Ebrahim

    2016-11-01

    In this paper, a subsystem consisting of a microstrip bandpass filter and a microstrip low noise amplifier (LNA) is designed for WLAN applications. The proposed filter has a small implementation area (49 mm2), small insertion loss (0.08 dB) and wide fractional bandwidth (FBW) (61%). To design the proposed LNA, the compact microstrip cells, an field effect transistor, and only a lumped capacitor are used. It has a low supply voltage and a low return loss (-40 dB) at the operation frequency. The matching condition of the proposed subsystem is predicted using subsystem analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To design the proposed filter, the transmission matrix of the proposed resonator is obtained and analysed. The performance of the proposed ANN and ANFIS models is tested using the numerical data by four performance measures, namely the correlation coefficient (CC), the mean absolute error (MAE), the average percentage error (APE) and the root mean square error (RMSE). The obtained results show that these models are in good agreement with the numerical data, and a small error between the predicted values and numerical solution is obtained.

  15. Application on Self-Exciting Threshold Auto-Regressive Model to forecast quantity of water irrigational requirement for rice%门限自回归模型在水稻需水量预测中的应用

    Institute of Scientific and Technical Information of China (English)

    张焕昭; 韩军利; 唐依伟; 付强

    2003-01-01

    通过对三江平原井灌水稻各生育阶段需水量的长系列资料分析,建立了自激励门限自回归模型(SETAR MODEL),采用9个参数有效地描述了水稻需水量各个生育期在多种气象及其它影响因子的作用下的周期变化的非线性复杂系统.模型拟合与预测精度较高,可在灌区规划管理与优化水稻灌溉制度中应用.

  16. Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M.B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M.S. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Vesovic, B.V. [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)

    1997-12-01

    This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.

  17. The Forecast of Tax Indicators Based on Vector Auto-regression Model%基于向量自回归模型的税务指标预测

    Institute of Scientific and Technical Information of China (English)

    江莉; 张瑞坤

    2011-01-01

    Asset-liability ratio is an important indicator to examine the financial status of the enterprises, and its prediction is of concern to a lot of enterprises. Four asset-liability ratio of the linear impact of the main factors is selected because many factors have an effect on the asset-liability ratio. According to the asset-liability ratio data of a company for two years, VAR model for the forecast of the next month's asset-liability ratio is chose variabled and established,and achieved good results,the error rate of less than 2%.%资产负债率是检查企业财务状况的一个重要指标,它的预测是很多企业所关心的.由于影响资产负债率的因素很多,选取了4个影响资产负债率的线性主要因素.根据某公司两年的资产负债率数据,选择变量,建立向量自回归模型.对下一月份的资产负债率进行预测,取得了很好的结果,相对误差控制在1.5%以内.

  18. A Neuro Fuzzy Technique for Process Grain Scheduling of Parallel Jobs

    Directory of Open Access Journals (Sweden)

    S. V. Sudha

    2011-01-01

    Full Text Available Problem statement: We present development of neural network based fuzzy inference system for scheduling of parallel Jobs with the help of a real life workload data. The performance evaluation of a parallel system mainly depends on how the processes are co scheduled? Various co scheduling techniques available are First Come First Served, Gang Scheduling, Flexible Co Scheduling and Agile Algorithm Approach: In order to use a wide range of objective functions, we used a rule bases scheduling strategy. The rule system depends on scheduling results of the agile algorithm and classifies all possible scheduling states and assigns an appropriate scheduling strategy based on actual state. The rule bases were developed with the help of a real workload data. Results: With the help of rule base results, scheduling was done again, which is compared with the first come first served, gang scheduling, flexible co scheduling and agile algorithm. The results of scheduling showed the optimized results of agile algorithm with the help of neuro fuzzy optimization technique. Conclusion: The study confirmed that the Neuro Fuzzy Technique can be used as a better optimization tool for optimizing any scheduling algorithm, This optimization tool is used for agile algorithm which is further used for process grain scheduling of parallel jobs.

  19. Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network

    Directory of Open Access Journals (Sweden)

    Vitor Badiale Furlong

    2013-02-01

    Full Text Available In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days, number of clusters (10, 30 and 50 clusters and internal weight softening parameter (Sigma (0.30, 0.45 and 0.60. These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A and 18 (B days of culture growth. The validations demonstrated that in long-term experiments (Validation A the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B, Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.

  20. Securing jammed network using reliability behavior value through neuro-fuzzy analysis

    Indian Academy of Sciences (India)

    S Raja Ratna; R Ravi

    2015-06-01

    Wireless multi-hop networks are often exposed to serious physical layer jamming attack. In this attack, the jammer node corrupts the packet by injecting high level of noise and keeps the channel busy and thus blocks the legitimate communication. If multiple jammers collude together, this attack will become very severe. To prevent this attack, a simple yet effective Reliability Behavior Neuro-Fuzzy system has been proposed and it operates in three modules. In module one, each route node obtains its behavior value from the route path and neighboring paths using direct and indirect behavior observations. In module two, based on the behavior value, three factor identification methods have been presented to identify the reliability value of nodes. In module three, using the reliability value the route nodes are level positioned and classified into groups by a neuro-fuzzy classifier. By simulation studies, it is observed that the proposed scheme significantly not only identifies misbehaving nodes with higher detection rate and lower false positive and but also achieves higher network throughput and lower jamming throughput.

  1. Prediction of Compressive Strength of Self compacting Concrete with Flyash and Rice Husk Ash using Adaptive Neuro-fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    S. S, Pathak

    2012-10-01

    Full Text Available Self-compacting concrete is an innovative concrete that does not require vibration for placing and compaction. It is able to flow under its own weight, completely filling formwork and achieving full compaction even in congested reinforcement without segregation and bleeding. In the present study self compacting concrete mixes were developed using blend of fly ash and rice husk ash. Fresh properties of theses mixes were tested by using standards recommended by EFNARC (European Federation for Specialist Construction Chemicals and Concrete system. Compressive strength at 28 days was obtained for these mixes. This paper presents development of Adaptive Neuro-fuzzy Inference System (ANFIS model for predicting compressive strength of self compacting concrete using fly ash and rice husk ash. The input parameters used for model are cement, fly ash, rice husk ash and water content. Output parameter is compressive strength at 28 days. The results show that the implemented model is good at predicting compressive strength.

  2. A new battery capacity indicator for nickel-metal hydride battery powered electric vehicles using adaptive neuro-fuzzy inference system

    CERN Document Server

    Chau, K T; Chan, C C; Shen, W X

    2003-01-01

    This paper describes a new approach to estimate accurately the battery residual capacity (BRC) of the nickel-metal hydride (Ni-MH) battery for modern electric vehicles (EVs). The key to this approach is to model the Ni-MH battery in EVs by using the adaptive neuro-fuzzy inference system (ANFIS) with newly defined inputs and output. The inputs are the temperature and the discharged capacity distribution describing the discharge current profile, while the output is the state of available capacity (SOAC) representing the BRC. The estimated SOAC from ANFIS model and the measured SOAC from experiments are compared, and the results confirm that the proposed approach can provide an accurate estimation of the SOAC under variable discharge currents.

  3. Neuro-fuzzy based Controller for Solving Active Power Filter

    Directory of Open Access Journals (Sweden)

    Homayoun Ebrahimian

    2016-03-01

    Full Text Available In this paper, two soft computing techniques by fuzzy logic, neural network are used to design alternative control schemes for switching the APF active power filter (APF. The control of a shunt active power filter designed for harmonic and reactive current mitigation. Application of the mentioned model has been combined by an intelligent algorithm for improving the efficiency of proposed controller. Effectiveness of the proposed method has been applied over test case and shows the validity of proposed model.

  4. Auto-adaptative Robot-aided Therapy based in 3D Virtual Tasks controlled by a Supervised and Dynamic Neuro-Fuzzy System

    Directory of Open Access Journals (Sweden)

    Luis Daniel Lledó

    2015-03-01

    Full Text Available This paper presents an application formed by a classification method based on the architecture of ART neural network (Adaptive Resonance Theory and the Fuzzy Set Theory to classify physiological reactions in order to automatically and dynamically adapt a robot-assisted rehabilitation therapy to the patient needs, using a three-dimensional task in a virtual reality system. Firstly, the mathematical and structural model of the neuro-fuzzy classification method is described together with the signal and training data acquisition. Then, the virtual designed task with physics behavior and its development procedure are explained. Finally, the general architecture of the experimentation for the auto-adaptive therapy is presented using the classification method with the virtual reality exercise.

  5. 外商直接投资、知识产权保护与出口产业结构调整--基于联立方程和VAR模型实证分析%Foreign Direct Investment,Intellectual Property Protection and Industrial Restructure of Export---The Empirical Research Based on Simultaneous Equations and Vector Auto Regression Models

    Institute of Scientific and Technical Information of China (English)

    周游

    2014-01-01

    By constructing simultaneous equations and taking advantage of vector auto regression models,this paper makes empirical research on the interrelation among Chinese foreign direct investment,intellectual property protection and export industrial structure from 1990 to 2011. The result shows that:structural optimization of export industry is not conducive to at-tract foreign direct investment,but foreign direct investment is conducive to structural optimization of export industry;It is beneficial to the structural optimization of Chinese export industry and inflow of foreign direct investment by strengthening of intellectual property protection,simultaneously,the structural optimization of Chinese export industry and inflow of foreign direct investment promote the improvement of the level of protection of intellectual property rights.%通过构建联立方程,并利用VAR模型,对我国1990~2011年外商直接投资、知识产权保护和出口产业结构之间相互关系进行实证研究,结果表明:出口产业结构优化不利于我国吸引外商直接投资,但外商直接投资有利于我国出口产业结构优化;加强知识产权保护有利于我国出口产业结构优化和外商直接投资流入,同时出口产业结构优化和外商直接投资流入也促进了我国知识产权保护水平的提高。

  6. A neuro-fuzzy controlling algorithm for wind turbine

    Energy Technology Data Exchange (ETDEWEB)

    Li Lin [Tampere Univ. of Technology (Finland); Eriksson, J.T. [Tampere Univ. of Technology (Finland)

    1995-12-31

    The wind turbine control system is stochastic and nonlinear, offering a demanding field for different control methods. An improved and efficient controller will have great impact on the cost-effectiveness of the technology. In this article, a design method for a self-organizing fuzzy controller is discussed, which combines two popular computational intelligence techniques, neural networks and fuzzy logic. Based on acquisited dynamic parameters of the wind, it can effectively predict wind changes in speed and direction. Maximum power can always be extracted from the kinetic energy of the wind. Based on the stimulating experiments applying nonlinear dynamics to a `Variable Speed Fixed Angle` wind turbine, it is demonstrated that the proposed control model 3rd learning algorithm provide a predictable, stable and accurate performance. The robustness of the controller to system parameter variations and measurement disturbances is also discussed. (author)

  7. Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression

    Directory of Open Access Journals (Sweden)

    Faezehossadat Khademi

    2016-12-01

    Full Text Available Compressive strength of concrete, recognized as one of the most significant mechanical properties of concrete, is identified as one of the most essential factors for the quality assurance of concrete. In the current study, three different data-driven models, i.e., Artificial Neural Network (ANN, Adaptive Neuro-Fuzzy Inference System (ANFIS, and Multiple Linear Regression (MLR were used to predict the 28 days compressive strength of recycled aggregate concrete (RAC. Recycled aggregate is the current need of the hour owing to its environmental pleasant aspect of re-using the wastes due to construction. 14 different input parameters, including both dimensional and non-dimensional parameters, were used in this study for predicting the 28 days compressive strength of concrete. The present study concluded that estimation of 28 days compressive strength of recycled aggregate concrete was performed better by ANN and ANFIS in comparison to MLR. In other words, comparing the test step of all the three models, it can be concluded that the MLR model is better to be utilized for preliminary mix design of concrete, and ANN and ANFIS models are suggested to be used in the mix design optimization and in the case of higher accuracy necessities. In addition, the performance of data-driven models with and without the non-dimensional parameters is explored. It was observed that the data-driven models show better accuracy when the non-dimensional parameters were used as additional input parameters. Furthermore, the effect of each non-dimensional parameter on the performance of each data-driven model is investigated. Finally, the effect of number of input parameters on 28 days compressive strength of concrete is examined.

  8. Exploration of the Adaptive Neuro - Fuzzy Inference System Architecture and its Applications

    Directory of Open Access Journals (Sweden)

    Okereke Eze Aru

    2016-09-01

    Full Text Available In this paper we exhibited an architecture and essential learning process basic in fuzzy inference system and adaptive neuro fuzzy inference system which is a hybrid network implemented in framework of adaptive network. In genuine figuring environment, soft computing techniques including neural network, fuzzy logic algorithms have been generally used to infer a real choice utilizing given input or output information traits, ANFIS can build mapping taking into account both human learning and hybrid algorithms. This study includes investigation of ANFIS methodology. ANFIS procedure is utilized to display nonlinear functions, to control a standout amongst the most essential parameters of the impelling machine and anticipate a turbulent time arrangement, all yielding more viable, quicker result.

  9. Adaptive Neuro-Fuzzy Controller Experimental Design for DC Motor Connected to Unbalanced Load

    Directory of Open Access Journals (Sweden)

    Reza Nejati

    2007-09-01

    Full Text Available In two recent decades, fuzzy controllers have been used in controlling different systems successfully. In this article, a new method is given for controlling of permanent magnetic DC motor connected to unbalanced load. Imbalance of load leads to machine vibrations, fluctuation of power, making exhaustion in machine shaft, and equipment depreciation. In this article neuro-fuzzy controllers are used for controlling unbalanced load. Because of non-linear nature of load and machine, machine fluctuations are different in various speeds. For making controller adaptive with machine, using an artificial neural network, the input-output coefficients are be updated in any speed. Optimized coefficients obtained by using of direct search method, and with these coefficients, artificial neural network trained with Lauvenberg-Marcoardet method. Operational results obtained from developed system, shows the efficiency of given method.

  10. Adaptive Functional-Based Neuro-Fuzzy-PID Incremental Controller Structure

    Directory of Open Access Journals (Sweden)

    Ashraf Ahmed Fahmy

    2014-03-01

    Full Text Available This paper presents an adaptive functional-based Neuro-fuzzy-PID incremental (NFPID controller structure that can be tuned either offline or online according to required controller performance. First, differential membership functions are used to represent the fuzzy membership functions of the input-output space of the three term controller. Second, controller rules are generated based on the discrete proportional, derivative, and integral function for the fuzzy space. Finally, a fully differentiable fuzzy neural network is constructed to represent the developed controller for either offline or online controller parameter adaptation.  Two different adaptation methods are used for controller tuning, offline method based on controller transient performance cost function optimization using Bees Algorithm, and online method based on tracking error minimization using back-propagation with momentum algorithm. The proposed control system was tested to show the validity of the controller structure over a fixed PID controller gains to control SCARA type robot arm.

  11. Recognition of Handwritten Arabic words using a neuro-fuzzy network

    Science.gov (United States)

    Boukharouba, Abdelhak; Bennia, Abdelhak

    2008-06-01

    We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system. The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.

  12. PREDIKSI CUACA MENGGUNAKAN METODE CASE BASED REASONING DAN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

    Directory of Open Access Journals (Sweden)

    Ria Chaniago

    2014-01-01

    Full Text Available Weather is one of the nature elements that can influence decision making in human's life. Based on that issue, the author wants to make an application that is able to predict weather with good accuracy. The application is a weather forecasting system, using computer technology that implements expert system. The methods used are Adaptive Neuro Fuzzy Inference System (ANFIS and Case Based Reasoning (CBR, and a combination of both methods will applied to the system. The system also has learning methods like Backpropagation Error (BPE and Recursive Least Error (RLSE, to increase its accuracy. Clustering and data cleaning also done inside the system, as it needed by forecasting process to achieve a good result. K-Means is the clustering algorithm, while Box and Whisker Plot is the algorithm for data cleaning. The result from this project is to create a weather forecasting system with high accuracy.

  13. Training Hybrid Neuro-Fuzzy System to Infer Permeability in Wells on Maracaibo Lake, Venezuela

    CERN Document Server

    Hurtado, Nuri; Torres, Julio

    2014-01-01

    The high accuracy on inferrring of rocks properties, such as permeability ($k$), is a very useful study in the analysis of wells. This has led to development and use of empirical equations like Tixier, Timur, among others. In order to improve the inference of permeability we used a hybrid Neuro-Fuzzy System (NFS). The NFS allowed us to infer permeability of well, from data of porosity ($\\phi$) and water saturation ($Sw$). The work was performed with data from wells VCL-1021 (P21) and VCL-950 (P50), Block III, Maracaibo Lake, Venezuela. We evaluated the NFS equations ($k_{P50,i}(\\phi_i,Sw_i)$) with neighboring well data ($P21$), in order to verify the validity of the equations in the area. We have used ANFIS in MatLab.

  14. Using Hierarchical Adaptive Neuro Fuzzy Systems And Design Two New Edge Detectors In Noisy Images

    Directory of Open Access Journals (Sweden)

    M. H. Olyaee

    2013-10-01

    Full Text Available One of the most important topics in image processing is edge detection. Many methods have been proposed for this end but most of them have weak performance in noisy images because noise pixels are determined as edge. In this paper, two new methods are represented based on Hierarchical Adaptive Neuro Fuzzy Systems (HANFIS. Each method consists of desired number of HANFIS operators that receive the value of some neighbouring pixels and decide central pixel is edge or not. Simple train images are used in order to set internal parameters of each HANFIS operator. The presented methods are evaluated by some test images and compared with several popular edge detectors. The experimental results show that these methods are robust against impulse noise and extract edge pixels exactly.

  15. Supervised and dynamic neuro-fuzzy systems to classify physiological responses in robot-assisted neurorehabilitation.

    Science.gov (United States)

    Lledó, Luis D; Badesa, Francisco J; Almonacid, Miguel; Cano-Izquierdo, José M; Sabater-Navarro, José M; Fernández, Eduardo; Garcia-Aracil, Nicolás

    2015-01-01

    This paper presents the application of an Adaptive Resonance Theory (ART) based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions.

  16. Neuro-Fuzzy based Controller for a Three- Phase Four-Wire Shunt Active Power Filter

    Directory of Open Access Journals (Sweden)

    Mridul Jha

    2011-10-01

    Full Text Available This paper describes the application of a novel neuro-fuzzy based control strategy which is used in order to improve the Active Power Filter (APF dynamics to minimize the harmonics for wide range of variations of load current under various conditions. To improve dynamic behavior of a three phase four-wire shunt active power filter and its robustness under range of load variations, adaptive hysteresis band with instantaneous p-q theory is used with the inclusion of neural network filter for reference current generation and fuzzy logic controller for DC voltage control. The proposed control scheme for “split-capacitor” converter topology is simple and also capable of maintaining the compensated line currents balanced, irrespective of unbalancing in the source voltages & deviation in the capacitor voltages. The results presented in MATLAB-SIMULINK software in this paper clearly reflect the effectiveness of the proposed APF to meet the IEEE-519 standard recommendations on harmonic levels.

  17. Adaptive neuro-fuzzy logic analysis based on myoelectric signals for multifunction prosthesis control.

    Science.gov (United States)

    Favieiro, Gabriela W; Balbinot, Alexandre

    2011-01-01

    The myoelectric signal is a sign of control of the human body that contains the information of the user's intent to contract a muscle and, therefore, make a move. Studies shows that the Amputees are able to generate standardized myoelectric signals repeatedly before of the intention to perform a certain movement. This paper presents a study that investigates the use of forearm surface electromyography (sEMG) signals for classification of five distinguish movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an adaptive neuro-fuzzy inference system (ANFIS) to process signal features to recognize performed movements. The average accuracy reached for the classification of five motion classes was 86-98% for three subjects.

  18. Supervised and dynamic neuro-fuzzy systems to classify physiological responses in robot-assisted neurorehabilitation.

    Directory of Open Access Journals (Sweden)

    Luis D Lledó

    Full Text Available This paper presents the application of an Adaptive Resonance Theory (ART based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions.

  19. Decision Support System for the Intelligient Identification of Alzheimer using Neuro Fuzzy logic

    Directory of Open Access Journals (Sweden)

    Obi J.C

    2011-05-01

    Full Text Available Alzheimer Disease (AD is a form of dementia; it is a progressive, degenerative disease. Alzheimer is abrain disease that causes problems with memory, thinking and behavior. It is severe enough to interferewith daily activities. Alzheimer symptoms are characterized by memory loss that affects day-to-dayfunction, difficulty performing familiar tasks, problems with language, disorientation of time and place,poor or decreased judgment, problems with abstract thinking, misplacing things, changes in mood andbehavior, changes in personality and loss of initiative. Neuro-Fuzzy Logic explores approximationtechniques from neural networks to find the parameter of a fuzzy system. In this paper, the traditionalprocedure for the medical diagnosis of Alzheimer employed by physician is analyzed using neuro-fuzzyinference procedure. The proposed system is a useful decision support approach for the diagnosis ofAlzheimer.

  20. Active Head Motion Compensation of TMS Robotic System Using Neuro-Fuzzy Estimation

    Directory of Open Access Journals (Sweden)

    Wan Zakaria W.N.

    2016-01-01

    Full Text Available Transcranial Magnetic Stimulation (TMS allows neuroscientist to study human brain behaviour and also become an important technique for changing the activity of brain neurons and the functions they sub serve. However, conventional manual procedure and robotized TMS are currently unable to precisely position the TMS coil because of unconstrained subject’s head movement and excessive contact force between the coil and subject’s head. This paper addressed this challenge by proposing an adaptive neuro-fuzzy force control to enable low contact force with a moving target surface. A learning and adaption mechanism is included in the control scheme to improve position disturbance estimation. The results show the ability of the proposed force control scheme to compensate subject’s head motions while maintaining desired contact force, thus allowing for more accurate and repeatable TMS procedures.

  1. Ant colony optimization algorithm and its application to Neuro-Fuzzy controller design

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information.The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation.The results of function optimization show that the algorithm has good searching ability and high convergence speed.The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum.In order to avoid the combinatorial explosion of fuzzy.rules due to multivariable inputs,a state variable synthesis scheme is emploved to reduce the number of fuzzy rules greatly.The simulation results show that the designed controller can control the inverted pendulum successfully.

  2. FPGA implementation of neuro-fuzzy system with improved PSO learning.

    Science.gov (United States)

    Karakuzu, Cihan; Karakaya, Fuat; Çavuşlu, Mehmet Ali

    2016-07-01

    This paper presents the first hardware implementation of neuro-fuzzy system (NFS) with its metaheuristic learning ability on field programmable gate array (FPGA). Metaheuristic learning of NFS for all of its parameters is accomplished by using the improved particle swarm optimization (iPSO). As a second novelty, a new functional approach, which does not require any memory and multiplier usage, is proposed for the Gaussian membership functions of NFS. NFS and its learning using iPSO are implemented on Xilinx Virtex5 xc5vlx110-3ff1153 and efficiency of the proposed implementation tested on two dynamic system identification problems and licence plate detection problem as a practical application. Results indicate that proposed NFS implementation and membership function approximation is as effective as the other approaches available in the literature but requires less hardware resources.

  3. Potential of neuro-fuzzy methodology to estimate noise level of wind turbines

    Science.gov (United States)

    Nikolić, Vlastimir; Petković, Dalibor; Por, Lip Yee; Shamshirband, Shahaboddin; Zamani, Mazdak; Ćojbašić, Žarko; Motamedi, Shervin

    2016-01-01

    Wind turbines noise effect became large problem because of increasing of wind farms numbers since renewable energy becomes the most influential energy sources. However, wind turbine noise generation and propagation is not understandable in all aspects. Mechanical noise of wind turbines can be ignored since aerodynamic noise of wind turbine blades is the main source of the noise generation. Numerical simulations of the noise effects of the wind turbine can be very challenging task. Therefore in this article soft computing method is used to evaluate noise level of wind turbines. The main goal of the study is to estimate wind turbine noise in regard of wind speed at different heights and for different sound frequency. Adaptive neuro-fuzzy inference system (ANFIS) is used to estimate the wind turbine noise levels.

  4. REPLACEMENT SPARE PART INVENTORY MONITORING USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

    Directory of Open Access Journals (Sweden)

    Hartono Hartono

    2016-01-01

    Full Text Available Abstract   The amount of inventory is determined on the basis of the demand. So that users can know the demand forecasts need to be done on the request. This study uses the data to implement a replacement parts on the electronic module production equipment in the telecommunications transmission systems, switching, access and power, ie by replacing the electronic module in the system is trouble  or damaged parts of a good electronic module spare parts inventory, while the faulty electronic modules shipped to the Repair Center for repaired again, so that the results of these improvements can replenish spare part  inventory. Parameters speed on improvement process of electronic module broken (repaired, in the form of an average repair time at the repair centers, in order to get back into the electronic module that is ready for used as spare parts in compliance with the safe supply inventory  warehouse.  This research using the method  of  Adaptive Neuro Fuzzy Inference System (ANFIS in developing a decision support system for inventory control of spare parts available in Warehouse Inventory taking into account several parameters supporters, namely demand, improvement and fulfillment of spare parts and repair time. This study uses a recycling input parameter repair faulty electronic module of the customer to immediately replace the module in inventory warehouse,  do improvements in the Repair Center. So the acceleration restoration factor is very influential as the input spare parts inventory supply in the warehouse and using the Adaptive Neuro-Fuzzy Inference System (ANFIS method.   Keywords: ANFIS, inventory control, replacement

  5. Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

    Directory of Open Access Journals (Sweden)

    P. Bhattacharya

    2007-11-01

    Full Text Available To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually, there is a need for the machine to understand the human state, especially cognitive state, when the human's operation task demands an intensive cognitive activity. Due to a well-known fact with the human being, a highly uncertain cognitive state and behavior as well as expressions or cues, the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper, we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example. The proposed method has, in particular, the following new features. First, human expressions are classified into four categories: (i casual or contextual feature, (ii contact feature, (iii contactless feature, and (iv performance feature. Second, the fuzzy neural network technique, in particular Takagi-Sugeno-Kang (TSK model, is employed to cope with uncertain behaviors. Third, the sensor fusion technique, in particular ordered weighted aggregation (OWA, is integrated with the TSK model in such a way that cues are taken as inputs to the TSK model, and then the outputs of the TSK are fused by the OWA which gives outputs corresponding to particular cognitive states under interest (e.g., fatigue. We call this method TSK-OWA. Validation of the TSK-OWA, performed in the Northeastern University vehicle drive simulator, has shown that the proposed method is promising to be a general tool for human cognitive state inferring and a special tool for the driver fatigue detection.

  6. Implementation of Hybrid Neuro-fuzzy Classifier%混合神经模糊分类器的实现

    Institute of Scientific and Technical Information of China (English)

    刘淑英

    2013-01-01

    Artificial neural network and fuzzy system were considered the main components of computation intelligence,the hybrid system about them was one of study topics in recent years. Classification is a research focus in data analysis,as data is complicated and diversi-fied,the requirements for classification will be increasingly high,sometimes only by experience and professional knowledge not to accu-rately classify. In view of their powerful data analysis functions,using neuro-fuzzy algorithm for data analysis will be meaningful and useful. In this paper,fuzzy C-means clustering algorithm model and Gath-Geva clustering algorithm model are proposed for the parame-ter classification,which is simulated,and obtain good results.%人工神经网络与模糊系统是计算智能的核心内容,二者的混合系统是近年来的一个研究热点。分类是数据分析中的研究重点,随着数据的复杂化和多样化,对分类的要求越来越高,有时仅凭经验和专业知识难以确切地进行分类,因此研究如何运用神经模糊分类算法进行数据分析具有重要意义与实用价值。鉴于其强大的数据分析功能,研究中采用模糊C均值聚类算法和Gath-Geva聚类算法对数据进行分类,并对测试数据进行仿真试验,其测试结果良好。

  7. Comparison between genetic fuzzy system and neuro fuzzy system to select oil wells for hydraulic fracturing; Comparacao entre genetic fuzzy system e neuro fuzzy system para selecao de pocos de petroleo para fraturamento hidraulico

    Energy Technology Data Exchange (ETDEWEB)

    Castro, Antonio Orestes de Salvo [PETROBRAS, Rio de Janeiro, RJ (Brazil); Ferreira Filho, Virgilio Jose Martins [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)

    2004-07-01

    The hydraulic fracture operation is wide used to increase the oil wells production and to reduce formation damage. Reservoir studies and engineer analysis are made to select the wells for this kind of operation. As the reservoir parameters have some diffuses characteristics, Fuzzy Inference Systems (SIF) have been tested for this selection processes in the last few years. This paper compares the performance of a neuro fuzzy system and a genetic fuzzy system used for hydraulic Fracture well selection, with knowledge acquisition from an operational data base to set the SIF membership functions. The training data and the validation data used were the same for both systems. We concluded that, in despite of the genetic fuzzy system would be a younger process, it got better results than the neuro fuzzy system. Another conclusion was that, as the genetic fuzzy system can work with constraints, the membership functions setting kept the consistency of variables linguistic values. (author)

  8. Drought prediction using co-active neuro-fuzzy inference system, validation, and uncertainty analysis (case study: Birjand, Iran)

    Science.gov (United States)

    Memarian, Hadi; Pourreza Bilondi, Mohsen; Rezaei, Majid

    2016-08-01

    This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1 + 2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1 + 2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1 + 2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1 + 2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS

  9. Automatic Assessing of Tremor Severity Using Nonlinear Dynamics, Artificial Neural Networks and Neuro-Fuzzy Classifier

    Directory of Open Access Journals (Sweden)

    GEMAN, O.

    2014-02-01

    Full Text Available Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP and an Adaptive Neuro-Fuzzy Classifier (ANFC. In general, the results may be expressed as a prognostic (risk degree to contact PD.

  10. Adaptive Neuro-Fuzzy Based Gain Controller for Erbium-Doped Fiber Amplifiers

    Directory of Open Access Journals (Sweden)

    YUCEL, M.

    2017-02-01

    Full Text Available Erbium-doped fiber amplifiers (EDFA must have a flat gain profile which is a very important parameter such as wavelength division multiplexing (WDM and dense WDM (DWDM applications for long-haul optical communication systems and networks. For this reason, it is crucial to hold a stable signal power per optical channel. For the purpose of overcoming performance decline of optical networks and long-haul optical systems, the gain of the EDFA must be controlled for it to be fixed at a high speed. In this study, due to the signal power attenuation in long-haul fiber optic communication systems and non-equal signal amplification in each channel, an automatic gain controller (AGC is designed based on the adaptive neuro-fuzzy inference system (ANFIS for EDFAs. The intelligent gain controller is implemented and the performance of this new electronic control method is demonstrated. The proposed ANFIS-based AGC-EDFA uses the experimental dataset to produce the ANFIS-based sets and the rule base. Laser diode currents are predicted within the accuracy rating over 98 percent with the proposed ANFIS-based system. Upon comparing ANFIS-based AGC-EDFA and experimental results, they were found to be very close and compatible.

  11. Design and Implementation of Neuro-Fuzzy Controller Using FPGA for Sun Tracking System

    Directory of Open Access Journals (Sweden)

    Ammar A. Aldair

    2016-12-01

    Full Text Available Nowadays, renewable energy is being used increasingly because of the global warming and destruction of the environment. Therefore, the studies are concentrating on gain of maximum power from this energy such as the solar energy. A sun tracker is device which rotates a photovoltaic (PV panel to the sun to get the maximum power. Disturbances which are originated by passing the clouds are one of great challenges in design of the controller in addition to the losses power due to energy consumption in the motors and lifetime limitation of the sun tracker. In this paper, the neuro-fuzzy controller has been designed and implemented using Field Programmable Gate Array (FPGA board for dual axis sun tracker based on optical sensors to orient the PV panel by two linear actuators. The experimental results reveal that proposed controller is more robust than fuzzy logic controller and proportional-integral (PI controller since it has been trained offline using Matlab tool box to overcome those disturbances. The proposed controller can track the sun trajectory effectively, where the experimental results reveal that dual axis sun tracker power can collect 50.6% more daily power than fixed angle panel. Whilst one axis sun tracker power can collect 39.4 % more daily power than fixed angle panel. Hence, dual axis sun tracker can collect 8 % more daily power than one axis sun tracker.

  12. An Improvement of Empirical Risk Functional in Neuro-Fuzzy Classifier

    Directory of Open Access Journals (Sweden)

    Elham Zamani

    2013-09-01

    Full Text Available This paper suggests a new method to improve of Empirical Risk Functional . Empirical Risk Functional acts as cost function for training neuro-fuzzy classifiers. Empirical risk minimization seeks the function that best fits the training data and it is equivalent to maximum likelihood estimation. The name of this cost function is Approximate Differentiable Empirical Risk Functional (ADERF.This function enables us to use a differentiable approximation of the misclassification rate so that the Empirical Risk Minimization Principle formulated in Statistical Learning Theory can be applied. Also there is a learning algorithm based on ADERF. With our new method,more component of output vector of fuzzy classifier map to 1.By evaluating the effects of the proposed method, we can see the convergence speed of the learning algorithm and the classification accuracy are improved,and causes improved ADERF. The effects of improved ADERF, was illustrated. Experimental results on a number of benchmark classification tasks and comparison between approaches are provided

  13. NF-SAVO: Neuro-Fuzzy system for Arabic Video OCR

    Directory of Open Access Journals (Sweden)

    Mohamed Ben Halima

    2012-10-01

    Full Text Available In this paper we propose a robust approach for text extraction and recognition from video clips which is called Neuro-Fuzzy system for Arabic Video OCR. In Arabic video text recognition, a number of noise components provide the text relatively more complicated to separate from the background. Further, the characters can be moving or presented in a diversity of colors, sizes and fonts that are not uniform. Added to this, is the fact that the background is usually moving making text extraction a more intricate process. Video include two kinds of text, scene text and artificial text. Scene text is usually text that becomes part of the scene itself as it is recorded at the time of filming the scene. But artificial text is produced separately and away from the scene and is laid over it at a later stage or during the post processing time. The emergence of artificial text is consequently vigilantly directed. This type of text carries with it important information that helps in video referencing, indexing and retrieval.

  14. Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.

    Science.gov (United States)

    Shamshirband, Shahaboddin; Petković, Dalibor; Hashim, Roslan; Motamedi, Shervin

    2014-01-01

    Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

  15. Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach

    Science.gov (United States)

    Julie, E. Golden; Selvi, S. Tamil

    2016-01-01

    Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes. PMID:26881269

  16. Performance analysis of electronic power transformer based on neuro-fuzzy controller.

    Science.gov (United States)

    Acikgoz, Hakan; Kececioglu, O Fatih; Yildiz, Ceyhun; Gani, Ahmet; Sekkeli, Mustafa

    2016-01-01

    In recent years, electronic power transformer (EPT), which is also called solid state transformer, has attracted great interest and has been used in place of the conventional power transformers. These transformers have many important functions as high unity power factor, low harmonic distortion, constant DC bus voltage, regulated output voltage and compensation capability. In this study, proposed EPT structure contains a three-phase pulse width modulation rectifier that converts 800 Vrms AC to 2000 V DC bus at input stage, a dual active bridge converter that provides 400 V DC bus with 5:1 high frequency transformer at isolation stage and a three-phase two level inverter that is used to obtain AC output at output stage. In order to enhance dynamic performance of EPT structure, neuro fuzzy controllers which have durable and nonlinear nature are used in input and isolation stages instead of PI controllers. The main aim of EPT structure with the proposed controller is to improve the stability of power system and to provide faster response against disturbances. Moreover, a number of simulation results are carried out to verify EPT structure designed in MATLAB/Simulink environment and to analyze compensation ability for voltage harmonics, voltage flicker and voltage sag/swell conditions.

  17. A novel approach for exposure assessment in air pollution epidemiological studies using neuro-fuzzy inference systems: Comparison of exposure estimates and exposure-health associations.

    Science.gov (United States)

    Blanes-Vidal, Victoria; Cantuaria, Manuella Lech; Nadimi, Esmaeil S

    2017-04-01

    Many epidemiological studies have used proximity to sources as air pollution exposure assessment method. However, proximity measures are not generally good surrogates because of their complex non-linear relationship with exposures. Neuro-fuzzy inference systems (NFIS) can be used to map complex non-linear systems, but its usefulness in exposure assessment has not been extensively explored. We present a novel approach for exposure assessment using NFIS, where the inputs of the model were easily-obtainable proximity measures, and the output was residential exposure to an air pollutant. We applied it to a case-study on NH3 pollution, and compared health effects and exposures estimated from NFIS, with those obtained from emission-dispersion models, and linear and non-linear regression proximity models, using 10-fold cross validation. The agreement between emission-dispersion and NFIS exposures was high (Root-mean-square error (RMSE) =0.275, correlation coefficient (r)=0.91) and resulted in similar health effect estimates. Linear models showed poor performance (RMSE=0.527, r=0.59), while non-linear regression models resulted in heterocedasticity, non-normality and clustered data. NFIS could be a useful tool for estimating individual air pollution exposures in epidemiological studies on large populations, when emission-dispersion data are not available. The tradeoff between simplicity and accuracy needs to be considered.

  18. Adaptive Neuro-Fuzzy Inference system analysis on adsorption studies of Reactive Red 198 from aqueous solution by SBA-15/CTAB composite

    Science.gov (United States)

    Aghajani, Khadijeh; Tayebi, Habib-Allah

    2017-01-01

    In this study, the Mesoporous material SBA-15 were synthesized and then, the surface was modified by the surfactant Cetyltrimethylammoniumbromide (CTAB). Finally, the obtained adsorbent was used in order to remove Reactive Red 198 (RR 198) from aqueous solution. Transmission electron microscope (TEM), Fourier transform infra-red spectroscopy (FTIR), Thermogravimetric analysis (TGA), X-ray diffraction (XRD), and BET were utilized for the purpose of examining the structural characteristics of obtained adsorbent. Parameters affecting the removal of RR 198 such as pH, the amount of adsorbent, and contact time were investigated at various temperatures and were also optimized. The obtained optimized condition is as follows: pH = 2, time = 60 min and adsorbent dose = 1 g/l. Moreover, a predictive model based on ANFIS for predicting the adsorption amount according to the input variables is presented. The presented model can be used for predicting the adsorption rate based on the input variables include temperature, pH, time, dosage, concentration. The error between actual and approximated output confirm the high accuracy of the proposed model in the prediction process. This fact results in cost reduction because prediction can be done without resorting to costly experimental efforts. SBA-15, CTAB, Reactive Red 198, adsorption study, Adaptive Neuro-Fuzzy Inference systems (ANFIS).

  19. Design of an expert system based on neuro-fuzzy inference analyzer for on-line microstructural characterization using magnetic NDT method

    Energy Technology Data Exchange (ETDEWEB)

    Ghanei, S., E-mail: Sadegh.Ghanei@yahoo.com [Department of Materials Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square, Mashhad (Iran, Islamic Republic of); Vafaeenezhad, H. [Centre of Excellence for High Strength Alloys Technology (CEHSAT), School of Metallurgical and Materials Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran (Iran, Islamic Republic of); Kashefi, M. [Department of Materials Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square, Mashhad (Iran, Islamic Republic of); Eivani, A.R. [Centre of Excellence for High Strength Alloys Technology (CEHSAT), School of Metallurgical and Materials Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran (Iran, Islamic Republic of); Mazinani, M. [Department of Materials Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square, Mashhad (Iran, Islamic Republic of)

    2015-04-01

    Tracing microstructural evolution has a significant importance and priority in manufacturing lines of dual-phase steels. In this paper, an artificial intelligence method is presented for on-line microstructural characterization of dual-phase steels. A new method for microstructure characterization based on the theory of magnetic Barkhausen noise nondestructive testing method is introduced using adaptive neuro-fuzzy inference system (ANFIS). In order to predict the accurate martensite volume fraction of dual-phase steels while eliminating the effect and interference of frequency on the magnetic Barkhausen noise outputs, the magnetic responses were fed into the ANFIS structure in terms of position, height and width of the Barkhausen profiles. The results showed that ANFIS approach has the potential to detect and characterize microstructural evolution while the considerable effect of the frequency on magnetic outputs is overlooked. In fact implementing multiple outputs simultaneously enables ANFIS to approach to the accurate results using only height, position and width of the magnetic Barkhausen noise peaks without knowing the value of the used frequency. - Highlights: • New NDT system for microstructural evaluation based on MBN using ANFIS modeling. • Sensitivity of magnetic Barkhausen noise to microstructure changes of the DP steels. • Accurate prediction of martensite by feeding multiple MBN outputs simultaneously. • Obtaining the modeled output without knowing the amount of the used frequency.

  20. Evaluation of artificial neural network (ANN and adaptive neuro-fuzzy inference system (ANFIS methods in prediction of global solar radiation

    Directory of Open Access Journals (Sweden)

    AA Sabziparvar

    2011-03-01

    Full Text Available Solar radiation is an important climate parameter which can affect hydrological and meteorological processes. This parameter is a key element in development of solar energy application studies. The purpose of this study is the assessment of artificial intelligence techniques in prediction of solar radiation (Rs using artificial neural network (ANN and adaptive neuro-fuzzy inference system (ANFIS. Minimum temperature, maximum temperature, average relative humidity, sunshine hours and daily solar radiation recorded in four synoptic stations (Esfahan, Urmieh, Shiraz and Kerman were used during the period 1992-2006. The results showed that ANN and ANFIS intelligent models are powerful tools in prediction of global solar radiation for the selected stations. Prediction by ANN was found to be more accurate than ANFIS. Also, the accuracy of prediction in Kerman with higher sunny hours was better than other stations (R2> 0.9. Additionally, using linear regression model, the most effective factors affecting Rs in each site was introduced. The results revealed that sunshine hour is the most important determining parameter affecting surface solar radiation. In contrast, in most sites minimum air temperature and mean relative humidity showed the least effect on surface global solar radiation.

  1. The Identification Level of Security, usability and Transparency Effects on Trust in B2C Commercial Websites Using Adaptive Neuro Fuzzy Inference System (ANFIS

    Directory of Open Access Journals (Sweden)

    Mehrbakhsh Nilashi, Mohammad Fathian, Mohammad Reza Gholamian, Othman Bin Ibrahim, Alireza Khoshraftar

    2011-08-01

    Full Text Available With the rapid development of Internet, the number of online customers is growing fast. Thisgrowth is supported by spreading of Internet usage around the globe. However, the questionof security and trust within e-commerce has always been in doubt. This study generatesgeneral knowledge about e-commerce. This study specifically gives an overview tounderstand different factors about security and trust between companies and theirconsumers. In order to Three e-stores and their websites were examined based on the modelproposed .This study also mentions that security and trust work parallel and close to eachother. If a consumer feels that an online deal is secured and they can trust the seller, it leadsto a confident e-commerce’s trade. The main focus of this study is to find out a suitable wayto resolve security and trust issues that make e-commerce an uncertain market place for allparties. The findings of this study indicate that, character of security is regarded as the mostimportant to building trust of B2C websites. The proposed model applies Adaptive Neuro-Fuzzy model to get the desired results. Two questionnaires were used in this study. The firstquestionnaire was developed for e-commerce experts, and the second one was designed forthe customers of commercial websites. Also, Expert Choice is used to determine the priorityof factors in the first questionnaire, and MATLAB and Excel are used for developing theFuzzy rules. Finally, the Fuzzy logical kit was used to analyze the generated factors in themodel.

  2. Neuro-fuzzy decoding of sensory information from ensembles of simultaneously recorded dorsal root ganglion neurons for functional electrical stimulation applications

    Science.gov (United States)

    Rigosa, J.; Weber, D. J.; Prochazka, A.; Stein, R. B.; Micera, S.

    2011-08-01

    Functional electrical stimulation (FES) is used to improve motor function after injury to the central nervous system. Some FES systems use artificial sensors to switch between finite control states. To optimize FES control of the complex behavior of the musculo-skeletal system in activities of daily life, it is highly desirable to implement feedback control. In theory, sensory neural signals could provide the required control signals. Recent studies have demonstrated the feasibility of deriving limb-state estimates from the firing rates of primary afferent neurons recorded in dorsal root ganglia (DRG). These studies used multiple linear regression (MLR) methods to generate estimates of limb position and velocity based on a weighted sum of firing rates in an ensemble of simultaneously recorded DRG neurons. The aim of this study was to test whether the use of a neuro-fuzzy (NF) algorithm (the generalized dynamic fuzzy neural networks (GD-FNN)) could improve the performance, robustness and ability to generalize from training to test sets compared to the MLR technique. NF and MLR decoding methods were applied to ensemble DRG recordings obtained during passive and active limb movements in anesthetized and freely moving cats. The GD-FNN model provided more accurate estimates of limb state and generalized better to novel movement patterns. Future efforts will focus on implementing these neural recording and decoding methods in real time to provide closed-loop control of FES using the information extracted from sensory neurons.

  3. Implementasi Adaptive Neuro-Fuzzy Inference System (Anfis untuk Peramalan Pemakaian Air di Perusahaan Daerah Air Minum Tirta Moedal Semarang

    Directory of Open Access Journals (Sweden)

    Ulfatun Hani'ah

    2016-06-01

    Full Text Available Peramalan pemakaian air pada bulan januari 2015 sampai April 2015 dapat dilakukan menggunakan perhitungan matematika dengan bantuan ilmu komputer. Metode yang digunakan adalah Adaptive Neuro Fuzzy Inference System (ANFIS dengan bantuan software MATLAB. Untuk pengujian program, dilakukan percobaan dengan memasukkan variabel klas = 2, maksimum epoh = 100, error = 10-6, rentang nilai learning rate = 0.6 sampai 0.9, dan rentang nilai momentum = 0.6 sampai 0.9. Simpulan yang diperoleh adalah bahwa implementasi metode Adaptive Neuro-Fuzzy Inference System dalam peramalan pemakaian air yang pertama adalah membuat rancangan flowchart, melakukan clustering data menggunakan fuzzy C-Mean, menentukan neuron tiap-tiap lapisan, mencari nilai parameter dengan menggunakan LSE rekursif, lalu penentuan perhitungan error menggunakan sum square error (SSE dan membuat sistem peramalan pemakaian air dengan software MATLAB. Setelah dilakukan percobaan hasil yang menunjukkan SSE paling kecil adalah nilai learning rate 0.9 dan momentum 0.6 dengan SSE 0.0080107. Hasil peramalan pemakaian air pada bulan Januari adalah 3.836.138m3, bulan Februari adalah 3.595.188m3, bulan Maret adalah 3.596.416 m3, dan bulan April adalah 3.776.833 m3. 

  4. Application of adaptive neuro-fuzzy inference system in motor soft start%自适应神经模糊推理系统在电动机软启动中的应用

    Institute of Scientific and Technical Information of China (English)

    李冬辉; 王莹莹; 马禹新

    2012-01-01

    Aimed at addressing serious grid impact entirely due to the impact of electricity resulting from direct start of induction motor,this paper introduces the application of the adaptive neuro-fuzzy inference system to the control of motor soft start.The method renders it possible to give a fuller play to the ability of adaptive learning of neural networks and fuzzy inference without the need to master the exact model of the object,and finally achieve the intelligent control of motor.The method consists of using the relationship of motor speed,load torque and the firing angle as training samples,and applying the hybrid learning algorithm to adjust the premise parameters and conclusion parameters,generating the fuzzy rules automatically and building the adaptive neuro-fuzzy inference system,and generating the appropriate thyristor trigger angle according to the given motor speed and torque.The simulation analysis shows that,the adaptive neuro-fuzzy inference system after training can afford a better control of motor speed,and thus promises to make possible the soft start of fan or pump load motor.%异步电动机直接启动产生的冲击电流会造成严重的电网冲击,因此提出将自适应神经模糊推理系统应用到电动机软启动控制中,充分发挥神经网络自适应学习和模糊推理不要求掌握被控对象精确模型处理结构化知识的能力,实现电动机软启动的智能控制。利用电机转速、负载转矩、触发角的对应关系作为训练样本,采用混合学习算法调整前提参数和结论参数,自动产生模糊规则,构建自适应神经模糊推理系统,根据给定的电机转速和转矩产生合适的晶闸管触发角。经仿真分析,结果表明:训练构建的自适应神经模糊推理系统能够很好地进行电机的速度控制,可以实现风机或泵类负载电动机的软启动。

  5. Sub-module Short Circuit Fault Diagnosis in Modular Multilevel Converter Based on Wavelet Transform and Adaptive Neuro Fuzzy Inference System

    DEFF Research Database (Denmark)

    Liu, Hui; Loh, Poh Chiang; Blaabjerg, Frede

    2015-01-01

    for continuous operation and post-fault maintenance. In this article, a fault diagnosis technique is proposed for the short circuit fault in a modular multi-level converter sub-module using the wavelet transform and adaptive neuro fuzzy inference system. The fault features are extracted from output phase voltage...

  6. Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network Estimador neuro-fuzzy de concentração diária de biomassa da microalga Synechococcus nidulans

    Directory of Open Access Journals (Sweden)

    Vitor Badiale Furlong

    2013-02-01

    Full Text Available In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days, number of clusters (10, 30 and 50 clusters and internal weight softening parameter (Sigma (0.30, 0.45 and 0.60. These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A and 18 (B days of culture growth. The validations demonstrated that in long-term experiments (Validation A the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B, Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.Neste trabalho, foi construído um estimador neuro-fuzzy da concentração de biomassa da microalga Synechococcus nidulans a partir de concentrações iniciais da batelada, visando possibilitar a predição da produtividade. Nove experimentos em réplica foram realizados. O crescimento foi acompanhado diariamente pela transmitância do meio e mantido até o final da fase exponencial de crescimento. O treinamento das redes ocorreu segundo delineamento experimental 3³, os fatores foram o número de dias no vetor de entrada (3, 5 e 7 dias, o número de clusters (10, 30 e 50 clusters e o valor de abrandamento do filtro interno (Sigma (0,30, 0,45 e 0,60. A variável resposta foi o somatório do erro quadrático das validações. Estas possuíam 24 (A

  7. Analyses of the most influential factors for vibration monitoring of planetary power transmissions in pellet mills by adaptive neuro-fuzzy technique

    Science.gov (United States)

    Milovančević, Miloš; Nikolić, Vlastimir; Anđelković, Boban

    2017-01-01

    Vibration-based structural health monitoring is widely recognized as an attractive strategy for early damage detection in civil structures. Vibration monitoring and prediction is important for any system since it can save many unpredictable behaviors of the system. If the vibration monitoring is properly managed, that can ensure economic and safe operations. Potentials for further improvement of vibration monitoring lie in the improvement of current control strategies. One of the options is the introduction of model predictive control. Multistep ahead predictive models of vibration are a starting point for creating a successful model predictive strategy. For the purpose of this article, predictive models of are created for vibration monitoring of planetary power transmissions in pellet mills. The models were developed using the novel method based on ANFIS (adaptive neuro fuzzy inference system). The aim of this study is to investigate the potential of ANFIS for selecting the most relevant variables for predictive models of vibration monitoring of pellet mills power transmission. The vibration data are collected by PIC (Programmable Interface Controller) microcontrollers. The goal of the predictive vibration monitoring of planetary power transmissions in pellet mills is to indicate deterioration in the vibration of the power transmissions before the actual failure occurs. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of vibration monitoring. It was also used to select the minimal input subset of variables from the initial set of input variables - current and lagged variables (up to 11 steps) of vibration. The obtained results could be used for simplification of predictive methods so as to avoid multiple input variables. It was preferable to used models with less inputs because of overfitting between training and testing data. While the obtained results are promising, further work is

  8. Adaptive neuro-fuzzy interface system for gap acceptance behavior of right-turning vehicles at partially controlled T-intersections

    Institute of Scientific and Technical Information of China (English)

    Jayant P. Sangole; Gopal R. Patil

    2014-01-01

    Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed coun-tries. Intersections with no specific priority to any move-ment, known as uncontrolled intersections, are common in India. Limited priority is observed at a few intersections, where priorities are perceived by drivers based on geom-etry, traffic volume, and speed on the approaches of intersection. Analyzing such intersections is complex because the overall traffic behavior is the result of drivers, vehicles, and traffic flow characteristics. Fuzzy theory has been widely used to analyze similar situations. This paper describes the application of adaptive neuro-fuzzy interface system (ANFIS) to the modeling of gap acceptance behavior of right-turning vehicles at limited priority T-intersections (in India, vehicles are driven on the left side of a road). Field data are collected using video cameras at four T-intersections having limited priority. The data extracted include gap/lag, subject vehicle type, conflicting vehicle type, and driver’s decision (accepted/rejected). ANFIS models are developed by using 80% of the extracted data (total data observations for major road right-turning vehicles are 722 and 1,066 for minor road right-turning vehicles) and remaining are used for model vali-dation. Four different combinations of input variables are considered for major and minor road right turnings sepa-rately. Correct prediction by ANFIS models ranges from 75.17% to 82.16% for major road right turning and 87.20% to 88.62% for minor road right turning. The models developed in this paper can be used in the dynamic estimation of gap acceptance in traffic simulation models.

  9. Analysis prediction of Indonesian banks (BCA, BNI, MANDIRI) using adaptive neuro-fuzzy inference system (ANFIS) and investment strategies

    Science.gov (United States)

    Trianto, Andriantama Budi; Hadi, I. M.; Liong, The Houw; Purqon, Acep

    2015-09-01

    Indonesian economical development is growing well. It has effect for their invesment in Banks and the stock market. In this study, we perform prediction for the three blue chips of Indonesian bank i.e. BCA, BNI, and MANDIRI by using the method of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Takagi-Sugeno rules and Generalized bell (Gbell) as the membership function. Our results show that ANFIS perform good prediction with RMSE for BCA of 27, BNI of 5.29, and MANDIRI of 13.41, respectively. Furthermore, we develop an active strategy to gain more benefit. We compare between passive strategy versus active strategy. Our results shows that for the passive strategy gains 13 million rupiah, while for the active strategy gains 47 million rupiah in one year. The active investment strategy significantly shows gaining multiple benefit than the passive one.

  10. Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

    Directory of Open Access Journals (Sweden)

    Zhixian Yang

    2014-01-01

    Full Text Available Background electroencephalography (EEG, recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE and sample entropy (SampEn in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.

  11. Improving the vector auto regression technique for time-series link prediction by using support vector machine

    Directory of Open Access Journals (Sweden)

    Co Jan Miles

    2016-01-01

    Full Text Available Predicting links between the nodes of a graph has become an important Data Mining task because of its direct applications to biology, social networking, communication surveillance, and other domains. Recent literature in time-series link prediction has shown that the Vector Auto Regression (VAR technique is one of the most accurate for this problem. In this study, we apply Support Vector Machine (SVM to improve the VAR technique that uses an unweighted adjacency matrix along with 5 matrices: Common Neighbor (CN, Adamic-Adar (AA, Jaccard’s Coefficient (JC, Preferential Attachment (PA, and Research Allocation Index (RA. A DBLP dataset covering the years from 2003 until 2013 was collected and transformed into time-sliced graph representations. The appropriate matrices were computed from these graphs, mapped to the feature space, and then used to build baseline VAR models with lag of 2 and some corresponding SVM classifiers. Using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC as the main fitness metric, the average result of 82.04% for the VAR was improved to 84.78% with SVM. Additional experiments to handle the highly imbalanced dataset by oversampling with SMOTE and undersampling with K-means clusters, however, did not improve the average AUC-ROC of the baseline SVM.

  12. Estimation of Flow Duration Curve for Ungauged Catchments using Adaptive Neuro-Fuzzy Inference System and Map Correlation Method: A Case Study from Turkey

    Science.gov (United States)

    Kentel, E.; Dogulu, N.

    2015-12-01

    In Turkey the experience and data required for a hydrological model setup is limited and very often not available. Moreover there are many ungauged catchments where there are also many planned projects aimed at utilization of water resources including development of existing hydropower potential. This situation makes runoff prediction at locations with lack of data and ungauged locations where small hydropower plants, reservoirs, etc. are planned an increasingly significant challenge and concern in the country. Flow duration curves have many practical applications in hydrology and integrated water resources management. Estimation of flood duration curve (FDC) at ungauged locations is essential, particularly for hydropower feasibility studies and selection of the installed capacities. In this study, we test and compare the performances of two methods for estimating FDCs in the Western Black Sea catchment, Turkey: (i) FDC based on Map Correlation Method (MCM) flow estimates. MCM is a recently proposed method (Archfield and Vogel, 2010) which uses geospatial information to estimate flow. Flow measurements of stream gauging stations nearby the ungauged location are the only data requirement for this method. This fact makes MCM very attractive for flow estimation in Turkey, (ii) Adaptive Neuro-Fuzzy Inference System (ANFIS) is a data-driven method which is used to relate FDC to a number of variables representing catchment and climate characteristics. However, it`s ease of implementation makes it very useful for practical purposes. Both methods use easily collectable data and are computationally efficient. Comparison of the results is realized based on two different measures: the root mean squared error (RMSE) and the Nash-Sutcliffe Efficiency (NSE) value. Ref: Archfield, S. A., and R. M. Vogel (2010), Map correlation method: Selection of a reference streamgage to estimate daily streamflow at ungaged catchments, Water Resour. Res., 46, W10513, doi:10.1029/2009WR008481.

  13. Adaptive neuro-fuzzy inference system (ANFIS) to predict CI engine parameters fueled with nano-particles additive to diesel fuel

    Science.gov (United States)

    Ghanbari, M.; Najafi, G.; Ghobadian, B.; Mamat, R.; Noor, M. M.; Moosavian, A.

    2015-12-01

    This paper studies the use of adaptive neuro-fuzzy inference system (ANFIS) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training and testing data. For ANFIS modelling, Gaussian curve membership function (gaussmf) and 200 training epochs (iteration) were found to be optimum choices for training process. The results demonstrate that ANFIS is capable of predicting the diesel engine performance and emissions. In the experimental step, Carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) with nanostructure were prepared and added as additive to the diesel fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel fuel, increased diesel engine power and torque output. For nano-diesel it was found that the brake specific fuel consumption (bsfc) was decreased compared to the net diesel fuel. The results proved that with increase of nano particles concentrations (from 40 ppm to 120 ppm) in diesel fuel, CO2 emission increased. CO emission in diesel fuel with nano-particles was lower significantly compared to pure diesel fuel. UHC emission with silver nano-diesel blended fuel decreased while with fuels that contains CNT nano particles increased. The trend of NOx emission was inverse compared to the UHC emission. With adding nano particles to the blended fuels, NOx increased compared to the net diesel fuel. The tests revealed that silver & CNT nano particles can be used as additive in diesel fuel to improve combustion of the fuel and reduce the exhaust emissions significantly.

  14. Tomato grading system using machine vision technology and neuro-fuzzy networks (ANFIS

    Directory of Open Access Journals (Sweden)

    H Izadi

    2016-04-01

    Full Text Available Introduction: The quality of agricultural products is associated with their color, size and health, grading of fruits is regarded as an important step in post-harvest processing. In most cases, manual sorting inspections depends on available manpower, time consuming and their accuracy could not be guaranteed. Machine Vision is known to be a useful tool for external features measurement (e.g. size, shape, color and defects and in recent century, Machine Vision technology has been used for shape sorting. The main purpose of this study was to develop new method for tomato grading and sorting using Neuro-fuzzy system (ANFIS and to compare the accuracies of the ANFIS predicted results with those suggested by a human expert. Materials and Methods: In this study, a total of 300 image of tomatoes (Rev ground was randomly harvested, classified in 3 ripeness stage, 3 sizes and 2 health. The grading and sorting mechanism consisted of a lighting chamber (cloudy sky, lighting source and a digital camera connected to a computer. The images were recorded in a special chamber with an indirect radiation (cloudy sky with four florescent lampson each sides and camera lens was entire to lighting chamber by a hole which was only entranced to outer and covered by a camera lens. Three types of features were extracted from final images; Shap, color and texture. To receive these features, we need to have images both in color and binary format in procedure shown in Figure 1. For the first group; characteristics of the images were analysis that could offer information an surface area (S.A., maximum diameter (Dmax, minimum diameter (Dmin and average diameters. Considering to the importance of the color in acceptance of food quality by consumers, the following classification was conducted to estimate the apparent color of the tomato; 1. Classified as red (red > 90% 2. Classified as red light (red or bold pink 60-90% 3. Classified as pink (red 30-60% 4. Classified as Turning

  15. Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; SubbaRao; Harish, N.; Lokesha

    Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models...

  16. Adaptive Neuro-Fuzzy Inference System Approach for the Automatic Screening of Diabetic Retinopathy in Fundus Images

    Directory of Open Access Journals (Sweden)

    S. Kavitha

    2011-01-01

    Full Text Available Problem statement: Diabetic retinopathy is one of the most significant factors contributing to blindness and so early diagnosis and timely treatment is particularly important to prevent visual loss. Approach: An integrated approach for extraction of blood vessels and exudates detection was proposed to screen diabetic retinopathy. An automated classifier was developed based on Adaptive Neuro-Fuzzy Inference System (ANFIS to differentiate between normal and nonproliferative eyes from the quantitative assessment of monocular fundus images. Feature extraction was performed on the preprocessed fundus images. Structure of Blood vessels was extracted using Multiscale analysis. Hard Exudates were detected using CIE Color channel transformation, Entropy Thresholding and Improved Connected Component Analysis from the fundus images. Features like Wall to Lumen ratio in blood vessels, Texture, Homogeneity properties and area occupied by Hard Exudates, were given as input to ANFIS.ANFIS was trained with Back propagation in combination with the least squares method. Proposed method was evaluated on 200 real time images comprising 70 normal and 130 retinopathic eyes. Results and Conclusion: All of the results were validated with ground truths obtained from expert ophthalmologists. Quantitative performance of the method, detected exudates with an accuracy of 99.5%. Receiver operating characteristic curve evaluated for real time images produced better results compared to the other state of the art methods. ANFIS provides best classification and can be used as a screening tool in the analysis and diagnosis of retinal images.

  17. A Neuro-fuzzy-sliding Mode Controller Using Nonlinear Sliding Surface Applied to the Coupled Tanks System

    Institute of Scientific and Technical Information of China (English)

    Ahcene Boubakir; Fares Boudjema; Salim Labiod

    2009-01-01

    The aim of this paper is to develop a neuro-fuzzy-sliding mode controller (NFSMC) with a nonlinear sliding surface for a coupled tank system.The main purpose is to eliminate the chattering phenomenon and to overcome the problem of the equivalent control computation.A first-order nonlinear sliding surface is presented,on which the developed sliding mode controller (SMC) is based.Mathematical proof for the stability and convergence of the system is presented.In order to reduce the chattering in SMC,a fixed boundary layer around the switch surface is used.Within the boundary layer,where the fuzzy logic control is applied,the chattering phenomenon,which is inherent in a sliding mode control,is avoided by smoothing the switch signal.Outside the boundary,the sliding mode control is applied to drive the system states into the boundary layer.Moreover,to compute the equivalent controller,a feed-forward neural network (NN) is used.The weights of the net are updated such that the corrective control term of the NFSMC goes to zero.Then,this NN also alleviates the chattering phenomenon because a big gain in the corrective control term produces a more serious chattering than a small gain.Experimental studies carried out on a coupled tank system indicate that the proposed approach is good for control applications.

  18. Vibration suppression control of smart piezoelectric rotating truss structure by parallel neuro-fuzzy control with genetic algorithm tuning

    Science.gov (United States)

    Lin, J.; Zheng, Y. B.

    2012-07-01

    The main goal of this paper is to develop a novel approach for vibration control on a piezoelectric rotating truss structure. This study will analyze the dynamics and control of a flexible structure system with multiple degrees of freedom, represented in this research as a clamped-free-free-free truss type plate rotated by motors. The controller has two separate feedback loops for tracking and damping, and the vibration suppression controller is independent of position tracking control. In addition to stabilizing the actual system, the proposed proportional-derivative (PD) control, based on genetic algorithm (GA) to seek the primary optimal control gain, must supplement a fuzzy control law to ensure a stable nonlinear system. This is done by using an intelligent fuzzy controller based on adaptive neuro-fuzzy inference system (ANFIS) with GA tuning to increase the efficiency of fuzzy control. The PD controller, in its assisting role, easily stabilized the linear system. The fuzzy controller rule base was then constructed based on PD performance-related knowledge. Experimental validation for such a structure demonstrates the effectiveness of the proposed controller. The broad range of problems discussed in this research will be found useful in civil, mechanical, and aerospace engineering, for flexible structures with multiple degree-of-freedom motion.

  19. Classification of Horse Gaits Using FCM-Based Neuro-Fuzzy Classifier from the Transformed Data Information of Inertial Sensor

    Science.gov (United States)

    Lee, Jae-Neung; Lee, Myung-Won; Byeon, Yeong-Hyeon; Lee, Won-Sik; Kwak, Keun-Chang

    2016-01-01

    In this study, we classify four horse gaits (walk, sitting trot, rising trot, canter) of three breeds of horse (Jeju, Warmblood, and Thoroughbred) using a neuro-fuzzy classifier (NFC) of the Takagi-Sugeno-Kang (TSK) type from data information transformed by a wavelet packet (WP). The design of the NFC is accomplished by using a fuzzy c-means (FCM) clustering algorithm that can solve the problem of dimensionality increase due to the flexible scatter partitioning. For this purpose, we use the rider’s hip motion from the sensor information collected by inertial sensors as feature data for the classification of a horse’s gaits. Furthermore, we develop a coaching system under both real horse riding and simulator environments and propose a method for analyzing the rider’s motion. Using the results of the analysis, the rider can be coached in the correct motion corresponding to the classified gait. To construct a motion database, the data collected from 16 inertial sensors attached to a motion capture suit worn by one of the country’s top-level horse riding experts were used. Experiments using the original motion data and the transformed motion data were conducted to evaluate the classification performance using various classifiers. The experimental results revealed that the presented FCM-NFC showed a better accuracy performance (97.5%) than a neural network classifier (NNC), naive Bayesian classifier (NBC), and radial basis function network classifier (RBFNC) for the transformed motion data. PMID:27171098

  20. Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system

    NARCIS (Netherlands)

    B. Tutmez (Bulent); Z. Hatipoglu (Z.); U. Kaymak (Uzay)

    2006-01-01

    textabstractElectrical conductivity is an important indicator for water quality assessment. Since the composition of mineral salts affects the electrical conductivity of groundwater, it is important to understand the relationships between mineral salt composition and electrical conductivity. In this

  1. Neuro-fuzzy modeling of multi-field surface neuroprostheses for hand grasp

    OpenAIRE

    Imatz Ojanguren, Eukene

    2016-01-01

    154 p. Las neuroprótesis aplican pulsos eléctricos a los nervios periféricos con el objetivo de sustituir funciones motrices/sensoriales perdidas, dando asistencia e influyendo positivamente en la rehabilitación motriz de personas con disfunciones motrices causadas por trastornos neurológicos. La complejidad de la neuroanatomía del antebrazo y la mano, su dimensionalidad, las diversas tareas no-cíclicas, la variabilidad de movimientos entre sujetos y la reducida selectividad de las neuropr...

  2. Forecast Research of Tax Revenue Income to Hebei Province--Based on the Cointegration Model and Auto-Regressive and Moving Average Model%基于协整-ARMA模型的河北省税收收入预测研究

    Institute of Scientific and Technical Information of China (English)

    朱云飞; 赵宁

    2012-01-01

    经济税源是税收收入的基础。相关理论和实践都表明,税收与经济增长之间存在着一种长期均衡关系。为模拟这种均衡关系,建立了税收与经济的协整模型;为预测税基发展趋势,建立了经济产值的ARMA模型;对经济税基的预测进一步测算税收收入的季度规模,并用部分历史数据和外推数据对预测模型进行了验证。经证明,模型的预测精度较高,可用于对未来各季度税收收入的实际预测。%Economic lax revenue source is the foundation of "tax revenue income. The long-term equilibrium relation be- tween the tax revenue and the economy grow has been corffinned by related theory and practice. In order to simulate the kind of equilibrium relation, we have established the co-integrafien model by the tax revenue and the economic output. In order to forecast the development situation of tax source, the ARMA model of economic output has been establishod. Through the prediction of tax revenue source, the quarter incomes scale can be forecasted and confirmed. It can be used on the future quarter revenue forecast because of its prediction accuracy confirmed by the relevant data validafion.

  3. Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

    Directory of Open Access Journals (Sweden)

    Julie M. David

    2013-11-01

    Full Text Available Learning Disability (LD is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

  4. A neuro-fuzzy warning system for combating cybersickness in the elderly caused by the virtual environment on a TFT-LCD.

    Science.gov (United States)

    Liu, Cheng-Li

    2009-05-01

    Only a few studies in the literature have focused on the effects of age on virtual environment (VE) sickness susceptibility and even less research was carried out focusing on the elderly. In general, the elderly usually browse VEs on a thin film transistor liquid crystal display (TFT-LCD) at home or somewhere, not a head-mounted display (HMD). While the TFT-LCD is used to present VEs, this set-up does not physically enclose the user. Therefore, this study investigated the factors that contribute to cybersickness among the elderly when immersed into a VE on TFT-LCD, including exposure durations, navigation rotating speeds and angle of inclination. Participants were elderly, with an average age of 69.5 years. The results of the first experiment showed that the rate of simulator sickness questionnaire (SSQ) scores increases significantly with navigational rotating speed and duration of exposure. However, the experimental data also showed that the rate of SSQ scores does not increase with the increase in angle of inclination. In applying these findings, the neuro-fuzzy technology was used to develop a neuro-fuzzy cybersickness-warning system integrating fuzzy logic reasoning and neural network learning. The contributing factors were navigational rotating speed and duration of exposure. The results of the second experiment showed that the proposed system can efficiently determine the level of cybersickness based on the associated subjective sickness estimates and combat cybersickness due to long exposure to a VE.

  5. 国际石油价格波动的结构性因素分析——基于结构向量自回归模型的实证研究%Analysis on structural factors of international price fluctuation of crude oil: empirical study based on structural vector auto regression model

    Institute of Scientific and Technical Information of China (English)

    李丹; 崔日明

    2011-01-01

    Since 1970s, the international price of crude oil fluctuated sharply for many times, and the analysis on its causation is always a hot topic in academic field. The history of international price fluctuation of crude oil is retrospected and devided into stages firstly, the structural factors of international price fluctuation of crude oil is analyzed secondly, and the causes of international price fluctuation of crude oil is analyzed empirically by applying SVAR model at last. The result shows that both shocks of general supply and short-term exogenous factors will inflence international price fluctuation of crude oil, but it is relatively not significant. Comparatively, the inflence of demand shock exceeds supply shock on international price fluctuation of crude oil, and the general demand shock evaluated by index of economic activities has not resulted in severe international price fluctuation of crude oil. It is showed that the uneven international price fluctuation of crude oil has no direct causation with the great increasing demand of crude oil in China in recent years.%20世纪70年代以来,国际油价经历多次剧烈波动,对于油价波动原因的分析一直是理论界的热点话题。首先对国际油价波动的历史进行梳理和阶段性划分,然后对国际油价波动的结构性因素进行分析,最后采用SVAR模型对国际油价波动的原因进行实证分析,结果显示一般供给冲击和短期外部因素冲击均会对国际油价波动产生影响,但其影响并不大。相比而言,需求冲击对国际油价的影响超过供给冲击,而且以经济活动指数衡量的一般需求冲击并未造成国际油价的剧烈波动。这说明,国际油价的异常波动与我国近年来对石油需求的大幅增加并没有直接的联系。

  6. Estimation of Switching Overvoltages on Transmission Lines Using Neuro-Fuzzy Method

    Directory of Open Access Journals (Sweden)

    Reza Shariatinasab

    2012-11-01

    Full Text Available Insulation failure caused by switching overvoltages (SOVs is one of the main sources of transmission lines’ outage, specially, on voltage levels of 345 kV and above. Therefore, the estimation of SOVs is vital in order to control and/or to reduce the switching–related outages. Due to the stochastic behavior of some of the parameters affecting on SOVs, the study of this phenomenon should be carried out based on a statistical study of the switching. Also, in the case of surge arrester installation on the transmission lines, depending on the location of arrester, voltage profile on line is changed and all the simulation should be performed for each new location of arresters, separately. One can conclude that this procedure is complex and time consuming. In this paper, a fuzzy based meta-model is presented which is be able to estimate the switching surge flashover rate (SSFOR, the maximum value of SOVs on the network and the location where the maximum overvoltage takes place. In the proposed meta model, the effect of altitude on SSFOR and the magnitude of SOVs is considered. This meta-model can be used, directly, for planning the insulation level of transmission lines in order to meet a certain number of outages and locating arresters on the region/nodes of the network of weak operation against SOVs. It is also possible to utilize the proposed meta model, indirectly, for assigning the optimal location of any specified set of arresters on the network without simulating of real network by a transient software, e.g. EMTP/ATP draw. The presented meta model can also be used in the operating stage to decide on the sequence of energizing and re-energizing of different transmission lines connected to the substations with the aim of reducing of maximum SOVs.

  7. Analysis Of A Neuro-Fuzzy Approach Of Air Pollution: Building A Case Study

    Directory of Open Access Journals (Sweden)

    Ciprian-Daniel NEAGU

    2001-12-01

    Full Text Available This work illustrates the necessity of an Artificial Intelligence (AI-based approach of air quality in urban and industrial areas. Some related results of Artificial Neural Networks (ANNs and Fuzzy Logic (FL for environmental data are considered: ANNs are proposed to the problem of short-term predicting of air pollutant concentrations in urban/industrial areas, with a special focus in the south-eastern Romania. The problems of designing a database about air quality in an urban/industrial area are discussed. First results confirm ANNs as an improvement of classical models and show the utility of ANNs in a well built air monitoring center.

  8. MI-ANFIS: A Multiple Instance Adaptive Neuro-Fuzzy Inference System

    Science.gov (United States)

    2015-08-02

    bag represents a molecule. Instances in a bag represent the different low-energy conformations of the molecule. Each instance consists of 166 features... book review],” Automatic Control, IEEE Transactions on, vol. 42, no. 10, pp. 1482–1484, 1997. [2] Y. Chai, L. Jia, and Z. Zhang, “Mamdani model based...instance learning,” in Advances in neural informa- tion processing systems, 2002, pp. 561–568. [13] Y. Li, D. M. Tax , R. P. Duin, and M. Loog

  9. Computation of Magnetic Field Distribution by Using an Adaptive Neuro-Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    P. Dhana Lakshmi

    2012-04-01

    Full Text Available This paper proposes a set of mathematical models presenting magnetic fields caused by operations of an extra high voltage (EHV transmission line under normal loading and short-circuit condi t ions . The mathematical model s are expressed in second-order partial differential equations derived by analyzing magnetic field distribution around a 500- kV power transmission line. The problem of study is intentionally two-dimensional due to the property of long line field distribution. To verify its use, i single-circuit and ii double-circuit, 500-kV power transmission lines have been employed for test. Finite element methods (FEM for solving wave equations have been exploited. The computer simulation based on the use of the FEM has been developed in MATLAB programming environment. This paper presents novel approach based on the use of adaptive network-based fuzzy inference system (ANFIS to estimate magnetic fields around an overhead power transmission lines. The ANFIS approach learns the rules and membership functions from training data. The hybrid system is tested by the use of the validation data. From all test cases, the calculation line of 1.0m above the ground level is set to investigate the magnetic fields acting on a human in c o m p a r a t i v e with ICNIRP standard.

  10. Neuro-Fuzzy Computational Technique to Control Load Frequency in Hydro-Thermal Interconnected Power System

    Science.gov (United States)

    Prakash, S.; Sinha, S. K.

    2015-09-01

    In this research work, two areas hydro-thermal power system connected through tie-lines is considered. The perturbation of frequencies at the areas and resulting tie line power flows arise due to unpredictable load variations that cause mismatch between the generated and demanded powers. Due to rising and falling power demand, the real and reactive power balance is harmed; hence frequency and voltage get deviated from nominal value. This necessitates designing of an accurate and fast controller to maintain the system parameters at nominal value. The main purpose of system generation control is to balance the system generation against the load and losses so that the desired frequency and power interchange between neighboring systems are maintained. The intelligent controllers like fuzzy logic, artificial neural network (ANN) and hybrid fuzzy neural network approaches are used for automatic generation control for the two area interconnected power systems. Area 1 consists of thermal reheat power plant whereas area 2 consists of hydro power plant with electric governor. Performance evaluation is carried out by using intelligent (ANFIS, ANN and fuzzy) control and conventional PI and PID control approaches. To enhance the performance of controller sliding surface i.e. variable structure control is included. The model of interconnected power system has been developed with all five types of said controllers and simulated using MATLAB/SIMULINK package. The performance of the intelligent controllers has been compared with the conventional PI and PID controllers for the interconnected power system. A comparison of ANFIS, ANN, Fuzzy and PI, PID based approaches shows the superiority of proposed ANFIS over ANN, fuzzy and PI, PID. Thus the hybrid fuzzy neural network controller has better dynamic response i.e., quick in operation, reduced error magnitude and minimized frequency transients.

  11. A Novel Technique for Maximum Power Point Tracking of a Photovoltaic Based on Sensing of Array Current Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

    Science.gov (United States)

    El-Zoghby, Helmy M.; Bendary, Ahmed F.

    2016-10-01

    Maximum Power Point Tracking (MPPT) is now widely used method in increasing the photovoltaic (PV) efficiency. The conventional MPPT methods have many problems concerning the accuracy, flexibility and efficiency. The MPP depends on the PV temperature and solar irradiation that randomly varied. In this paper an artificial intelligence based controller is presented through implementing of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to obtain maximum power from PV. The ANFIS inputs are the temperature and cell current, and the output is optimal voltage at maximum power. During operation the trained ANFIS senses the PV current using suitable sensor and also senses the temperature to determine the optimal operating voltage that corresponds to the current at MPP. This voltage is used to control the boost converter duty cycle. The MATLAB simulation results shows the effectiveness of the ANFIS with sensing the PV current in obtaining the MPPT from the PV.

  12. Estimation of the most influential factors on the laser cutting process heat affected zone (HAZ) by adaptive neuro-fuzzy technique

    Science.gov (United States)

    Petković, Dalibor; Nikolić, Vlastimir; Milovančević, Miloš; Lazov, Lyubomir

    2016-07-01

    Heat affected zone (HAZ) of the laser cutting process may be developed on the basis on combination of different factors. In this investigation was analyzed the HAZ forecasting based on the different laser cutting parameters. The main aim in this article was to analyze the influence of three inputs on the HAZ of the laser cutting process. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for HAZ forecasting. Three inputs are considered: laser power, cutting speed and gas pressure. According the results the cutting speed has the highest influence on the HAZ forecasting (RMSE: 0.0553). Gas pressure has the smallest influence on the HAZ forecasting (RMSE: 0.0801). The results can be used in order to simplify HAZ prediction and analyzing.

  13. Prediction of Zoonosis Incidence in Human using Seasonal Auto Regressive Integrated Moving Average (SARIMA)

    CERN Document Server

    Permanasari, Adhistya Erna; Dominic, Dhanapal Durai

    2009-01-01

    Zoonosis refers to the transmission of infectious diseases from animal to human. The increasing number of zoonosis incidence makes the great losses to lives, including humans and animals, and also the impact in social economic. It motivates development of a system that can predict the future number of zoonosis occurrences in human. This paper analyses and presents the use of Seasonal Autoregressive Integrated Moving Average (SARIMA) method for developing a forecasting model that able to support and provide prediction number of zoonosis human incidence. The dataset for model development was collected on a time series data of human tuberculosis occurrences in United States which comprises of fourteen years of monthly data obtained from a study published by Centers for Disease Control and Prevention (CDC). Several trial models of SARIMA were compared to obtain the most appropriate model. Then, diagnostic tests were used to determine model validity. The result showed that the SARIMA(9,0,14)(12,1,24)12 is the fitt...

  14. Modelling solar radiation reached to the Earth using ANFIS, NN-ARX, and empirical models (Case studies: Zahedan and Bojnurd stations)

    Science.gov (United States)

    Piri, Jamshid; Kisi, Ozgur

    2015-02-01

    The amount of incoming solar energy that crosses the Earth's atmosphere is called solar radiation. The solar radiation is a series of ultraviolet wavelengths including visible and infrared light. The solar rays at the Earth's surface is one of the key factor in water resources, environmental and agricultural modelling. Solar radiation is rarely measured by weather stations in Iran and other developing countries; as a result, many empirical approaches have been applied to estimate it by using other climatic parameters. In this study, non-linear models, adaptive neuro-fuzzy inference system (ANFIS) and neural network auto-regressive model with exogenous inputs (NN-ARX) along with empirical models, Angstrom and Hargreaves-Samani, have been used to estimate the solar radiation. The data was collected from two synoptic stations with different climatic conditions (Zahedan and Bojnurd) during the period of 5 and 7 years, respectively. These data contain sunshine hours, maximum temperature, minimum temperature, average relative humidity and solar radiation. The Angstrom and Hargreaves-Samani empirical models, respectively, based on sunshine hours and temperature were calibrated and evaluated in both stations. In order to train, test, and validate ANFIS and NNRX models, 60%, 25%, and 15% of the data were applied, respectively. The results of artificial intelligence models were compared with the empirical models. The findings showed that ANFIS (R2=0.90 and 0.97 for Zahedan and Bojnurd, respectively) and NN-ARX (R2=0.89 and 0.96 for Zahedan and Bojnurd, respectively) performed better than the empirical models in estimating daily solar radiation.

  15. Oil Price Volatility and Economic Growth in Nigeria: a Vector Auto-Regression (VAR Approach

    Directory of Open Access Journals (Sweden)

    Edesiri Godsday Okoro

    2014-02-01

    Full Text Available The study examined oil price volatility and economic growth in Nigeria linking oil price volatility, crude oil prices, oil revenue and Gross Domestic Product. Using quarterly data sourced from the Central Bank of Nigeria (CBN Statistical Bulletin and World Bank Indicators (various issues spanning 1980-2010, a non‐linear model of oil price volatility and economic growth was estimated using the VAR technique. The study revealed that oil price volatility has significantly influenced the level of economic growth in Nigeria although; the result additionally indicated a negative relationship between the oil price volatility and the level of economic growth. Furthermore, the result also showed that the Nigerian economy survived on crude oil, to such extent that the country‘s budget is tied to particular price of crude oil. This is not a good sign for a developing economy, more so that the country relies almost entirely on revenue of the oil sector as a source of foreign exchange earnings. This therefore portends some dangers for the economic survival of Nigeria. It was recommended amongst others that there should be a strong need for policy makers to focus on policy that will strengthen/stabilize the economy with specific focus on alternative sources of government revenue. Finally, there should be reduction in monetization of crude oil receipts (fiscal discipline, aggressive saving of proceeds from oil booms in future in order to withstand vicissitudes of oil price volatility in future.

  16. Visibility graph analysis for re-sampled time series from auto-regressive stochastic processes

    Science.gov (United States)

    Zhang, Rong; Zou, Yong; Zhou, Jie; Gao, Zhong-Ke; Guan, Shuguang

    2017-01-01

    Visibility graph (VG) and horizontal visibility graph (HVG) play a crucial role in modern complex network approaches to nonlinear time series analysis. However, depending on the underlying dynamic processes, it remains to characterize the exponents of presumably exponential degree distributions. It has been recently conjectured that there is a critical value of exponent λc = ln 3 / 2 , which separates chaotic from correlated stochastic processes. Here, we systematically apply (H)VG analysis to time series from autoregressive (AR) models, which confirms the hypothesis that an increased correlation length results in larger values of λ > λc. On the other hand, we numerically find a regime of negatively correlated process increments where λ < λc, which is in contrast to this hypothesis. Furthermore, by constructing graphs based on re-sampled time series, we find that network measures show non-trivial dependencies on the autocorrelation functions of the processes. We propose to choose the decorrelation time as the maximal re-sampling delay for the algorithm. Our results are detailed for time series from AR(1) and AR(2) processes.

  17. Use of an adaptive neuro-fuzzy inference system to obtain the correspondence among balance, gait, and depression for Parkinson's disease

    Science.gov (United States)

    Woo, Youngkeun; Lee, Juwon; Hwang, Sujin; Hong, Cheol Pyo

    2013-03-01

    The purpose of this study was to investigate the associations between gait performance, postural stability, and depression in patients with Parkinson's disease (PD) by using an adaptive neuro-fuzzy inference system (ANFIS). Twenty-two idiopathic PD patients were assessed during outpatient physical therapy by using three clinical tests: the Berg balance scale (BBS), Dynamic gait index (DGI), and Geriatric depression scale (GDS). Scores were determined from clinical observation and patient interviews, and associations among gait performance, postural stability, and depression in this PD population were evaluated. The DGI showed significant positive correlation with the BBS scores, and negative correlation with the GDS score. We assessed the relationship between the BBS score and the DGI results by using a multiple regression analysis. In this case, the GDS score was not significantly associated with the DGI, but the BBS and DGI results were. Strikingly, the ANFIS-estimated value of the DGI, based on the BBS and the GDS scores, significantly correlated with the walking ability determined by using the DGI in patients with Parkinson's disease. These findings suggest that the ANFIS techniques effectively reflect and explain the multidirectional phenomena or conditions of gait performance in patients with PD.

  18. Selection of the most influential factors on the water-jet assisted underwater laser process by adaptive neuro-fuzzy technique

    Science.gov (United States)

    Nikolić, Vlastimir; Petković, Dalibor; Lazov, Lyubomir; Milovančević, Miloš

    2016-07-01

    Water-jet assisted underwater laser cutting has shown some advantages as it produces much less turbulence, gas bubble and aerosols, resulting in a more gentle process. However, this process has relatively low efficiency due to different losses in water. It is important to determine which parameters are the most important for the process. In this investigation was analyzed the water-jet assisted underwater laser cutting parameters forecasting based on the different parameters. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for water-jet assisted underwater laser cutting parameters forecasting. Three inputs are considered: laser power, cutting speed and water-jet speed. The ANFIS process for variable selection was also implemented in order to detect the predominant factors affecting the forecasting of the water-jet assisted underwater laser cutting parameters. According to the results the combination of laser power cutting speed forms the most influential combination foe the prediction of water-jet assisted underwater laser cutting parameters. The best prediction was observed for the bottom kerf-width (R2 = 0.9653). The worst prediction was observed for dross area per unit length (R2 = 0.6804). According to the results, a greater improvement in estimation accuracy can be achieved by removing the unnecessary parameter.

  19. Neuro-fuzzy computing for vibration-based damage localization and severity estimation in an experimental wind turbine blade with superimposed operational effects

    Science.gov (United States)

    Hoell, Simon; Omenzetter, Piotr

    2016-04-01

    Fueled by increasing demand for carbon neutral energy, erections of ever larger wind turbines (WTs), with WT blades (WTBs) with higher flexibilities and lower buckling capacities lead to increasing operation and maintenance costs. This can be counteracted with efficient structural health monitoring (SHM), which allows scheduling maintenance actions according to the structural state and preventing dramatic failures. The present study proposes a novel multi-step approach for vibration-based structural damage localization and severity estimation for application in operating WTs. First, partial autocorrelation coefficients (PACCs) are estimated from vibrational responses. Second, principal component analysis is applied to PACCs from the healthy structure in order to calculate scores. Then, the scores are ranked with respect to their ability to differentiate different damage scenarios. This ranking information is used for constructing hierarchical adaptive neuro-fuzzy inference systems (HANFISs), where cross-validation is used to identify optimal numbers of hierarchy levels. Different HANFISs are created for the purposes of structural damage localization and severity estimation. For demonstrating the applicability of the approach, experimental data are superimposed with signals from numerical simulations to account for characteristics of operational noise. For the physical experiments, a small scale WTB is excited with a domestic fan and damage scenarios are introduced non-destructively by attaching small masses. Numerical simulations are also performed for a representative fully functional small WT operating in turbulent wind. The obtained results are promising for future applications of vibration-based SHM to facilitate improved safety and reliability of WTs at lower costs.

  20. Extraction of fetal electrocardiogram (ECG) by extended state Kalman filtering and adaptive neuro-fuzzy inference system (ANFIS) based on single channel abdominal recording

    Indian Academy of Sciences (India)

    D Panigrahy; P K Sahu

    2015-06-01

    Fetal electrocardiogram (ECG) gives information about the health status of fetus and so, an early diagnosis of any cardiac defect before delivery increases the effectiveness of appropriate treatment. In this paper, authors investigate the use of adaptive neuro-fuzzy inference system (ANFIS) with extended Kalman filter for fetal ECG extraction from one ECG signal recorded at the abdominal areas of the mother’s skin. The abdominal ECG is considered to be composite as it contains both mother’s and fetus’ ECG signals. We use extended Kalman filter framework to estimate the maternal component from abdominal ECG. The maternal component in the abdominal ECG signal is a nonlinear transformed version of maternal ECG. ANFIS network has been used to identify this nonlinear relationship, and to align the estimated maternal ECG signal with the maternal component in the abdominal ECG signal. Thus, we extract the fetal ECG component by subtracting the aligned version of the estimated maternal ECG from the abdominal signal. Our results demonstrate the effectiveness of the proposed technique in extracting the fetal ECG component from abdominal signal at different noise levels. The proposed technique is also validated on the extraction of fetal ECG from both actual abdominal recordings and synthetic abdominal recording.

  1. An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions

    Science.gov (United States)

    Ajay Kumar, M.; Srikanth, N.

    2014-03-01

    In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.

  2. An exploratory investigation of an adaptive neuro fuzzy inference system (ANFIS) for estimating hydrometeors from TRMM/TMI in synergy with TRMM/PR

    Science.gov (United States)

    Islam, Tanvir; Srivastava, Prashant K.; Rico-Ramirez, Miguel A.; Dai, Qiang; Han, Dawei; Gupta, Manika

    2014-08-01

    The authors have investigated an adaptive neuro fuzzy inference system (ANFIS) for the estimation of hydrometeors from the TRMM microwave imager (TMI). The proposed algorithm, named as Hydro-Rain algorithm, is developed in synergy with the TRMM precipitation radar (PR) observed hydrometeor information. The method retrieves rain rates by exploiting the synergistic relations between the TMI and PR observations in twofold steps. First, the fundamental hydrometeor parameters, liquid water path (LWP) and ice water path (IWP), are estimated from the TMI brightness temperatures. Next, the rain rates are estimated from the retrieved hydrometeor parameters (LWP and IWP). A comparison of the hydrometeor retrievals by the Hydro-Rain algorithm is done with the TRMM PR 2A25 and GPROF 2A12 algorithms. The results reveal that the Hydro-Rain algorithm has good skills in estimating hydrometeor paths LWP and IWP, as well as surface rain rate. An examination of the Hydro-Rain algorithm is also conducted on a super typhoon case, in which the Hydro-Rain has shown very good performance in reproducing the typhoon field. Nevertheless, the passive microwave based estimate of hydrometeors appears to suffer in high rain rate regimes, and as the rain rate increases, the discrepancies with hydrometeor estimates tend to increase as well.

  3. Aplicación de Técnicas Neuro-Difusas para el Diseño de un Controlador Application of Neuro-Fuzzy Techniques for the Design of a Controller

    Directory of Open Access Journals (Sweden)

    A. Noriega

    2005-01-01

    Full Text Available En este trabajo se presentan algunos esquemas de control neuro-difuso para el diseño de un controlador difuso simplificado de dos entradas y una salida. La simplificación introducida ha permitido lograr una importante reducción en el tiempo de cálculo de la señal de control, pero es posible que en algunos sistemas se pueda afectar el desempeño del sistema de control. Para resolver este problema se ha incorporado una red neuronal de manera que se pueda mejorar la calidad en el control y se pueda controlar procesos de dinámica compleja. Los resultados de las aplicaciones demuestran que se puede disponer de una metodología de control neuro-difuso general, aplicable a cualquier sistema.In this work some neuro-fuzzy control schemes for the design of a simplified controller of two inputs and one output are presented. This simplification has allowed getting an important reduction in the calculation control time but it is possible that this can affect the performance of the control system. To solve this problem a neural network has been incorporated so that the control quality can be improved and problems of complex dynamics can be solved. The results of the applications show that it is possible to have a neuro-fuzzy control methodology applicable to any system.

  4. Neuro-fuzzy based approach for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater

    Digital Repository Service at National Institute of Oceanography (India)

    Patil, S.G.; Mandal, S.; Hegde, A.V.; Alavandar, S.

    The ocean wave system in nature is very complicated and physical model studies on floating breakwaters are expensive and time consuming. Till now, there has not been available a simple mathematical model to predict the wave transmission through...

  5. An Integrated Intelligent Neuro-Fuzzy Algorithm for Long-Term Electricity Consumption: Cases of Selected EU Countries

    Directory of Open Access Journals (Sweden)

    Alireza Behrooznia

    2010-11-01

    Full Text Available This paper presents an adaptive-network-based fuzzy inference system (ANFISfor long-term natural Electric consumption prediction. Six models are proposed to forecastannual Electric demand. 104 ANFIS have been constructed and tested in order to find thebest ANFIS for Electric consumption. Two parameters have been considered in theconstruction and examination of plausible ANFIS models. The type of membership functionand the number of linguistic variables are two mentioned parameters. Six differentmembership functions are considered in building ANFIS, as follows: the built-inmembership function composed of the difference between two sigmoidal membershipfunctions (dsig, the Gaussian combination membership function (gauss2, the Gaussiancurve built-in membership function (gauss, the generalized bell-shaped built-inmembership function (gbell, the Π-shaped built-in membership function (pi, psig. Also, anumber for linguistic variables has been considered between 2 and 20. The proposedmodels consist of input variables such as: Gross Domestic Product (GDP and Population(POP. Six distinct models based on different inputs are defined. All of the trained ANFISare then compared with respect to the mean absolute percentage error (MAPE. To meetthe best performance of the intelligent based approaches, data are pre-processed (scaledand finally our outputs are post-processed (returned to its original scale. The ANFISmodel is capable of dealing with both complexity and uncertainty in the data set. To showthe applicability and superiority of the ANFIS, the actual Electric consumption inindustrialized nations including the Netherlands, Luxembourg, Ireland, and Italy from 1980to 2007 are considered. With the aid of an autoregressive model, the GDP and populationby 2015 is projected and then with yield value and best ANFIS model, Electric consumptionby 2015 is predicted.

  6. Análise dos efeitos dos parâmetros de configuração de um controlador neuro-fuzzy aplicado em um processo de neutralização

    OpenAIRE

    Débora Zenaide Gorri Mazzali

    2015-01-01

    Técnicas de Inteligência Artificial (IA) buscam imitar o raciocínio humano através da aplicação de regras lógicas, para um conjunto de dados disponível, de modo a chegar a uma forma mais eficiente de resolver problemas. Sendo um dos ramos da IA, a técnica neuro-fuzzy abordada neste estudo, será aplicada em controladores de processos que, por sua vez, são formados por estruturas de regras lógicas de difícil definição, pois existem inúmeras possibilidades de configurações que podem ser adotadas...

  7. Identification of Civil Engineering Structures using Vector ARMA Models

    DEFF Research Database (Denmark)

    Andersen, P.

    The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models.......The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models....

  8. 基于模糊神经算法的区域地下水盐分动态预测%Regional groundwater salinity dynamics forecasting based on neuro-fuzzy algorithm

    Institute of Scientific and Technical Information of China (English)

    余世鹏; 杨劲松; 刘广明; 姚荣江; 王相平

    2014-01-01

    为探讨前馈型人工神经网络BP-ANN(back propagation artificial neural network)和模糊神经NF (neuro-fuzzy)2种神经网络算法在区域地下水盐分动态预测中的应用过程与效果,首先通过经典统计分析确定区域地下水盐分动态的主要驱动因子以及可用的模型输入因子组合,采用“试错法”确定神经网络模型的最优结构,进而开展地下水盐分中长期动态的有效模拟预测。结果表明,在长江河口寅阳和大兴地区以降水动态为单输入的NF(5-gbellmf-160)和以降水与内河水盐分动态为双输入的NF(4-gaussmf-100)为最优预测模型。研究表明神经网络模型对地下水盐分动态的预测精度优于常规线性模型,其中,NF、BP-ANN、线性模型在寅阳测点的预测相关系数分别为0.565、0.445、0.261,在大兴测点的预测相关系数分别为0.886、0.784、0.543。与BP-ANN、线性模型相比,基于模糊神经算法的 NF 模型具有更好的误差纠错和仿真能力,在寅阳和大兴测点的预测误差分别降低了30%以上和50%以上。相关研究结果在区域水盐动态科学预警研究领域有较好地应用前景。%The study conducted a detailed analysis of the modeling processes and performances of 2 types of different neural network models including back propagation artificial neural network (BP-ANN) and neuro-fuzzy (NF), in the groundwater salinity dynamics forecasting. Firstly, the classical statistical analysis was used to determine the dominant driving factors of groundwater salinity dynamics and to reveal the available model inputs combinations. Then, the optimal neural network model structures were determined by the trial-and-error method and used to effectively forecast the mid-long term groundwater salinity dynamics. By our research, the idea of necessity in selecting the optimal NF model parameters of transfer functions, rule numbers and iteration steps was innovatively

  9. Application of time-lagged ensemble approach with auto-regressive processors to reduce uncertainties in peak discharge and timing

    Directory of Open Access Journals (Sweden)

    Kyung-Jin Kim

    2017-02-01

    An accuracy evaluation using observations from 2002 to 2009 found that the time-lagged ensemble approach alone produced significant bias but the AR processor reduced the relative error percentage of the peak discharge from 60% to 10% and also decreased the peak timing error from more than 10 h to less than 3 h, on average. The proposed methodology is easy and inexpensive to implement with the existing products and models and thus can be immediately activated until a new product for forecasted meteorological ensembles is officially issued in Korea.

  10. A neuro-fuzzy system to support in the diagnostic of epileptic events and non-epileptic events using different fuzzy arithmetical operations Um sistema neuro-difuso para auxiliar no diagnóstico de eventos epilépticos e eventos não epilépticos utilizando diferentes operações aritméticas difusas

    Directory of Open Access Journals (Sweden)

    Lucimar M.F. de Carvalho

    2008-06-01

    Full Text Available OBJECTIVE: To investigate different fuzzy arithmetical operations to support in the diagnostic of epileptic events and non epileptic events. METHOD: A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication architecture and an artificial neural network with backpropagation learning algorithm (ANNB. RESULTS: The study was composed by 244 patients with a bigger frequency of the feminine sex. The number of right decisions at the test phase, obtained by the NEFCLASS and ANNB was 83.60% and 90.16%, respectively. The best sensibility result was attained by NEFCLASS (84.90%; the best specificity result were attained by ANNB with 95.65%. CONCLUSION: The proposed neuro-fuzzy system combined the artificial neural network capabilities in the pattern classifications together with the fuzzy logic qualitative approach, leading to a bigger rate of system success.OBJETIVO: Investigar diferentes operações aritméticas difusas para auxíliar no diagnóstico de eventos epilépticos e eventos não-epilépticos. MÉTODO: Um sistema neuro-difuso foi desenvolvido utilizando a arquitetura NEFCLASS (NEuro Fuzzy CLASSIfication e uma rede neural artificial com o algoritmo de aprendizagem backpropagation (RNAB. RESULTADOS: A amostra estudada foi de 244 pacientes com maior freqüência no sexo feminino. O número de decisões corretas na fase de teste, obtidas através do NEFCLASS e RNAB foi de 83,60% e 90,16%, respectivamente. O melhor resultado de sensibilidade foi obtido com o NEFCLASS (84,90%; o melhor resultado de especificidade foi obtido com a RNAB (95,65%. CONCLUSÃO: O sistema neuro-difuso proposto combinou a capacidade das redes neurais artificiais na classificação de padrões juntamente com a abordagem qualitativa da logica difusa, levando a maior taxa de acertos do sistema.

  11. Video Smoke Detection Based on Adaptive Neuro-fuzzy Inference System%基于自适应神经模糊推理系统的视频烟雾检测

    Institute of Scientific and Technical Information of China (English)

    王涛; 刘渊; 谢振平

    2011-01-01

    This paper presents a video smoke detection algorithm based on Adaptive Neuro-fuzzy Inference System(ANFIS). The smoke features are extracted from video sequences, and the subtractive clustering is introduced to confirm the fuzzy rule number. The premise parameters and the consequent parameters are updated by hybrid learning rule. The fuzzy inference rules are obtained. Experimental results show that compared with the traditional BP neural network algorithm and Support Vector Machine(SVM) algorithm, the new algorithm has better performance on Receiver Operating Characteristic(ROC) curve.%提出一种基于自适应神经模糊推理系统的视频烟雾检测算法.从视频图像中提取烟雾特征,采用减法聚类确定模糊规则数,建立初始模糊系统.通过神经网络的自学习机制调整前提参数和结论参数,确定模糊推理规则.实验结果表明,与传统BP神经网络算法及支持向量机算法相比,该算法具有较优的ROC曲线特性.

  12. Fuzzy Control Strategies in Human Operator and Sport Modeling

    CERN Document Server

    Ivancevic, Tijana T; Markovic, Sasa

    2009-01-01

    The motivation behind mathematically modeling the human operator is to help explain the response characteristics of the complex dynamical system including the human manual controller. In this paper, we present two different fuzzy logic strategies for human operator and sport modeling: fixed fuzzy-logic inference control and adaptive fuzzy-logic control, including neuro-fuzzy-fractal control. As an application of the presented fuzzy strategies, we present a fuzzy-control based tennis simulator.

  13. Application of artificial intelligence models in water quality forecasting.

    Science.gov (United States)

    Yeon, I S; Kim, J H; Jun, K W

    2008-06-01

    The real-time data of the continuous water quality monitoring station at the Pyeongchang river was analyzed separately during the rainy period and non-rainy period. Total organic carbon data observed during the rainy period showed a greater mean value, maximum value and standard deviation than the data observed during the non-rainy period. Dissolved oxygen values during the rainy period were lower than those observed during the non-rainy period. It was analyzed that the discharge due to rain fall from the basin affects the change of the water quality. A model for the forecasting of water quality was constructed and applied using the neural network model and the adaptive neuro-fuzzy inference system. Regarding the models of levenberg-marquardt neural network, modular neural network and adaptive neuro-fuzzy inference system, all three models showed good results for the simulation of total organic carbon. The levenberg-marquardt neural network and modular neural network models showed better results than the adaptive neuro-fuzzy inference system model in the forecasting of dissolved oxygen. The modular neural network model, which was applied with the qualitative data of time in addition to quantitative data, showed the least error.

  14. Predictive Control Based on Neuro-fuzzy Model for CSTR system%基于神经模糊模型的CSTR系统预测控制

    Institute of Scientific and Technical Information of China (English)

    冯冬青; 张妍妍

    2008-01-01

    针对具有高度非线性特性的连续搅拌反应釜(CSTR)控制过程,研究了基于神经模糊模型的预测控制策略.利用神经模糊模型预测CSTR系统在一定预测时段的输出,基于进化规划(EP)的优化算法,使用神经模糊模型的预测输出来确定输入序列,实现对CSTR的PH值跟踪控制,仿真结果表明该方法可以获得满意的跟踪控制效果.

  15. MODEL PENGENDALIAN ASET NIRWUJUD DALAM MANAJEMEN SISTEM IRIGASI (Model of Controlling Intangible Assets in Irrigation System Management

    Directory of Open Access Journals (Sweden)

    Nugroho Tri Waskitho

    2013-03-01

    Full Text Available The research aimed at developing model of controlling intangible assets in irrigation system management. The research method consisted of two stages. The first stage was building the model of controlling intangible assets in irrigation system management using neuro-fuzzy. The model had three submodels: (i knowledge management, (ii intangible assets, and (iii performance of irrigation system. The second stage was evaluating the model in Sapon irrigation system in Kulon Progo, Yogyakarta. Data collecting was done by questionnaire and interview on nine Water Use Associations. Data analysis was done by Adaptive Neuro Fuzzy Inference System. The model had been evaluated by correlation coefficient, Mean Absolute Percentage Error and Root Mean Square Error. The research result indicated that the model of controlling intangible assets in irrigation system management could predict intangible assets and performance of irrigation system well. The model linked knowledge management, intangible assets and performance of irrigation system.  Knowledge management felt into four main components: (i learning organization, (ii principle of organization, (iii policy and strategy of organization, and (iv information and communication technology which controlling intangible assets in irrigation system. Intangible assets consisted of moral intelligence, emotional intelligence, creativity attitude, institutional culture, and farmer participation which  controlling effectiveness of irrigation system. Keywords: model, intangible assets, controlling, irrigation system, knowledge management   Tujuan penelitian adalah mengembangkan model pengendalian aset nirwujud dalam manajemen sistem irigasi. Metode penelitian terdiri dari dua tahap. Tahap pertama adalah pembangunan model pengendalian aset nirwujud dalam manajemen sistem irigasi dengan prinsip neuro-fuzzy. Model mempunyai tiga sub model yaitu manajemen pengetahuan, aset nirwujud dan kinerja sistem irigasi. Tahap kedua

  16. Study on fasART neuro-fuzzy networks for distinguishing the difficulty degree of top coal caving in steep seam

    Institute of Scientific and Technical Information of China (English)

    FENG Tao; ZHAO Fu-jun; LIN Jian

    2005-01-01

    Distinguishing the difficulty degree of top coal caving was a precondition of the popularization and application of the roadway sub-level caving in steep seam. Because of complexity and uncertainty of the coal seam, the expression of influence factors was difficulty with exact data. According to the fuzzy and uncertainty of influence factors, triangular fuzzy membership functions were adopted to carry out the factors ambiguity, of which the factors not only have the consistency of semantic meaning, but also dissolve sufficiently expert knowledge. Based on the properties and structures of fasART fuzzy neural networks of fuzzy logic system and practical needs, a simplified fasART model was put forward, stability and reliability of the network were improved, the deficiency of learning sampies and uncertainty of the factors were better treated. The method is of effective and practical value was identified by experiments.

  17. Students Classification With Adaptive Neuro Fuzzy

    Directory of Open Access Journals (Sweden)

    Mohammad Saber Iraji

    2012-07-01

    Full Text Available Identifying exceptional students for scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and performance. a system is proposed through Lvq networks methods, anfis method to separate these student on learning factor . In our proposed system, adaptive fuzzy neural network(anfis has less error and can be used as an effective alternative system for classifying students

  18. ANFIS Modelling of Flexible Plate Structure

    Directory of Open Access Journals (Sweden)

    A. A. M. Al-Khafaji

    2010-06-01

    Full Text Available This paper presented an investigation into the performance of system identification using an Adaptive Neuro-Fuzzy Inference System (ANFIS technique for the dynamic modelling of a twodimensional flexible plate structure. It is confirmed experimentally, using National Instrumentation (NI Data Acquisition System (DAQ and flexible plate test rig that ANFIS can be effectively used for modelling the system with highly accurate results. The accuracy of the modelling results is demonstrated through validation tests including training and test validation and correlation tests.

  19. Fluctuations of offshore wind generation: Statistical modelling

    DEFF Research Database (Denmark)

    Pinson, Pierre; Christensen, Lasse E.A.; Madsen, Henrik

    2007-01-01

    The magnitude of power fluctuations at large offshore wind farms has a significant impact on the control and management strategies of their power output. If focusing on the minute scale, one observes successive periods with smaller and larger power fluctuations. It seems that different regimes...... yield different behaviours of the wind power output. This paper concentrates on the statistical modelling of offshore power fluctuations, with particular emphasis on regime-switching models. More precisely, Self-Exciting Threshold AutoRegressive (SETAR), Smooth Transition AutoRegressive (STAR......) and Markov-Switching AutoRegressive (MSAR) models are considered. The particularities of these models are presented, as well as methods for the estimation of their parameters. Simulation results are given for the case of the Horns Rev and Nysted offshore wind farms in Denmark, for time-series of power...

  20. ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study.

    Science.gov (United States)

    Heddam, Salim; Bermad, Abdelmalek; Dechemi, Noureddine

    2012-04-01

    Coagulation is the most important stage in drinking water treatment processes for the maintenance of acceptable treated water quality and economic plant operation, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, pH, temperature, etc. As such, coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Traditionally, jar tests are used to determine the optimum coagulant dosage. However, this is expensive and time-consuming and does not enable responses to changes in raw water quality in real time. Modelling can be used to overcome these limitations. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for modelling of coagulant dosage in drinking water treatment plant of Boudouaou, Algeria. Six on-line variables of raw water quality including turbidity, conductivity, temperature, dissolved oxygen, ultraviolet absorbance, and the pH of water, and alum dosage were used to build the coagulant dosage model. Two ANFIS-based Neuro-fuzzy systems are presented. The two Neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system (FIS), named ANFIS-GRID, and (2) subtractive clustering based (FIS), named ANFIS-SUB. The low root mean square error and high correlation coefficient values were obtained with ANFIS-SUB method of a first-order Sugeno type inference. This study demonstrates that ANFIS-SUB outperforms ANFIS-GRID due to its simplicity in parameter selection and its fitness in the target problem.

  1. Neural model of gene regulatory network: a survey on supportive meta-heuristics.

    Science.gov (United States)

    Biswas, Surama; Acharyya, Sriyankar

    2016-06-01

    Gene regulatory network (GRN) is produced as a result of regulatory interactions between different genes through their coded proteins in cellular context. Having immense importance in disease detection and drug finding, GRN has been modelled through various mathematical and computational schemes and reported in survey articles. Neural and neuro-fuzzy models have been the focus of attraction in bioinformatics. Predominant use of meta-heuristic algorithms in training neural models has proved its excellence. Considering these facts, this paper is organized to survey neural modelling schemes of GRN and the efficacy of meta-heuristic algorithms towards parameter learning (i.e. weighting connections) within the model. This survey paper renders two different structure-related approaches to infer GRN which are global structure approach and substructure approach. It also describes two neural modelling schemes, such as artificial neural network/recurrent neural network based modelling and neuro-fuzzy modelling. The meta-heuristic algorithms applied so far to learn the structure and parameters of neutrally modelled GRN have been reviewed here.

  2. Identification and Modeling of Automotive Electrical Parking Brake System for SiL Simulation

    Institute of Scientific and Technical Information of China (English)

    PENG Yi-qiang; LI Wei; ZHANG Jin-lei

    2008-01-01

    To evaluate the software behavior of the electronic control unit (ECU) of automotive electrical parking brake (EPB), a software-in-the-loop (SiL) simulation system is built. The EPB is simulated by ARX (auto-regressive with auxiliary input) model, ARMAX (auto-regressive moving average with auxiliary input) model, and NNARMAX (neural network ARMAX) model. By system identification, the ARX(3,4,2), ARX(4,4,2), ARMAX(3,3,1,1), and ARMAX(4,4,3,2) models are derived. Validation results show that the four-order ARMAX model and the NNARMAX model better simulate the actuator of the EPB.

  3. Prediction models for performance and emissions of a dual fuel CI engine using ANFIS

    Indian Academy of Sciences (India)

    A Adarsh Rai; P Srinivasa Pai; B R Shrinivasa Rao

    2015-04-01

    Dual fuel engines are being used these days to overcome shortage of fossil fuels and fulfill stringent exhaust gas emission regulations. They have several advantages over conventional diesel engines. In this context, this paper makes use of experimental results obtained from a dual fuel engine for developing models to predict performance and emission parameters. Conventional modelling efforts to understand the relationships between the input and the output variables, requires thermodynamic analysis which is complex and time consuming. As a result, efforts have been made to use artificial intelligence modelling techniques like fuzzy logic, Artificial Neural Network (ANN), Genetic Algorithm (GA), etc. This paper uses a neuro fuzzy modelling technique, Adaptive Neuro Fuzzy Inference System (ANFIS) for developing prediction models for performance and emission parameter of a dual fuel engine. Percentage load, percentage Liquefied Petroleum Gas (LPG) and Injection Timing (IT) have been used as input parameters, whereas output parameters include Brake Specific Energy Consumption (BSEC), Brake Thermal Efficiency (BTE), Exhaust Gas Temperature (EGT) and smoke. In order to further improve the prediction accuracy of the model, GA has been used to optimize ANFIS. GA optimized ANFIS gives higher prediction accuracy of more than 90% for all parameters except for smoke, where there is a substantial improvement from 46.67% to 73.33%, when compared to conventional ANFIS model.

  4. Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling

    Energy Technology Data Exchange (ETDEWEB)

    Afshar, Mohammad [Islamic Azad University, Kharg (Iran, Islamic Republic of); Gholami, Amin [Petroleum University of Technology, Abadan (Iran, Islamic Republic of); Asoodeh, Mojtaba [Islamic Azad University, Birjand (Iran, Islamic Republic of)

    2014-03-15

    Bubble point pressure is a critical pressure-volume-temperature (PVT) property of reservoir fluid, which plays an important role in almost all tasks involved in reservoir and production engineering. We developed two sophisticated models to estimate bubble point pressure from gas specific gravity, oil gravity, solution gas oil ratio, and reservoir temperature. Neural network and adaptive neuro-fuzzy inference system are powerful tools for extracting the underlying dependency of a set of input/output data. However, the mentioned tools are in danger of sticking in local minima. The present study went further by optimizing fuzzy logic and neural network models using the genetic algorithm in charge of eliminating the risk of being exposed to local minima. This strategy is capable of significantly improving the accuracy of both neural network and fuzzy logic models. The proposed methodology was successfully applied to a dataset of 153 PVT data points. Results showed that the genetic algorithm can serve the neural network and neuro-fuzzy models from local minima trapping, which might occur through back-propagation algorithm.

  5. Elastodynamic shape modeler: a tool for defining the deformation behavior of virtual tissues.

    Science.gov (United States)

    Radetzky, A; Nürnberger, A; Pretschner, D P

    2000-01-01

    A main goal of surgical simulators is the creation of virtual training environments for prospective surgeons. Thus, students can rehearse the various steps of surgical procedures on a computer system without any risk to the patient. One main condition for realistic training is the simulated interaction with virtual medical devices, such as endoscopic instruments. In particular, the virtual deformation and transection of tissues are important. For this application, a neuro-fuzzy model has been developed, which allows the description of the visual and haptic deformation behavior of the simulated tissue by means of expert knowledge in the form of medical terms. Pathologic conditions affecting the visual and haptic tissue response can be easily changed by a medical specialist without mathematical knowledge. By using the personal computer-based program Elastodynamic Shape Modeler, these conditions can be adjusted via a graphical user interface. With a force feedback device, which is similar to a real laparoscopic instrument, virtual deformations can be performed and the resulting haptic feedback can be felt. Thus, use of neuro-fuzzy technologies for the definition and calculation of virtual deformations seems applicable to the simulation of surgical interventions in virtual environments.

  6. 基于向量自回归模型的微博隐式话题流行度预测%Vector Auto-regression Model Based Microblog Hidden Topic Popularity Prediction

    Institute of Scientific and Technical Information of China (English)

    段东圣; 李鹏霄; 李玉华; 李瑞轩

    2016-01-01

    现有话题流行度预测方法仅基于话题本身的特征进行流行度预测,未考虑不同话题间的相关性.然而在微博上下文不同的话题之间存在一定的相关性,特别是在同一个事件的不同话题之间.因此,文中利用动态话题模型探测微博中的隐式话题及其流行度时间序列,通过Jensen-Shannon散度和皮尔逊相关系数分别分析话题间的内容和时序相关度,然后在预测模型中引入话题时序相关性,提出基于向量自回归模型的微博隐式话题流行度预测算法.通过在真实微博数据上的实验分析可知,相比未考虑话题相关性的算法,文中算法具有更高的预测准确率和更好的模型拟合效果.

  7. 门限自回归模型及其在耕地总量预测中的应用%Threshold Auto-regressive Model and its Application in Gross Arable Land Prediction

    Institute of Scientific and Technical Information of China (English)

    胡华科

    2007-01-01

    利用梅州市55年来长时序耕地总量统计资料,引入门限自回归模型对耕地面积进行了预测,并和马尔可夫模型预测结果进行了比较.结果表明,通过门限值的控制作用,门限自回归模型有效地利用时序数据隐含的时序分段相依性这一重要信息,限制了模型误差,从而保证了预测性能的稳健性,有很高的短中期预测精度.

  8. 门限自回归模型及其在水文随机模拟中的应用%Application of Threshold Auto-regressive Model in Hydrologic Stochastic Simulation

    Institute of Scientific and Technical Information of China (English)

    王文圣; 袁鹏; 丁晶; 邓育仁

    2001-01-01

    为了客观描述日流量变化的非线性特性,将一种非线性时序模型--门限自回归模型引入日流量随机模拟.根据我国金沙江流域屏山站观测资料建立了日流量随机模拟的门限自回归模型.实用性检验结果表明,该模型用于模拟日流量过程是可行的,成果实用.这种尝试为日流量随机模拟提供了一种考虑日流量非线性变化特性的新模型.

  9. Threshold auto - regressive model study on the time domain signal of random acoustic loading%随机声载荷时域信号门限自回归模型研究

    Institute of Scientific and Technical Information of China (English)

    吴兆奇; 关蓬莱; 吴晓明

    2008-01-01

    利用航空发动机燃烧室噪声测试数据[1],采用门限自回归分析方法建立随机声载荷门限自回归模型SETAR(2;2;30,30),得到令人满意结果,并将拟合和预测均方误差与文献[1]非门限的自回归滑动平均模型ARMA(17,16)的结果进行了比较.

  10. APPLICATION OF PROJECTION PURSUIT THRESHOLD AUTO-REGRESSIVE MODEL TO PREDICTING SEA ICE CONDITIONS%投影寻踪门限自回归模型在海洋冰情预测中的应用

    Institute of Scientific and Technical Information of China (English)

    金菊良; 魏一鸣; 付强; 丁晶

    2002-01-01

    为预测海洋冰情时序这类非线性动力系统,提出了投影寻踪门限自回归(PPTAR)模型.用自相关分析技术确定预测因子,构造了新的投影指标函数,用门限回归(TR)模型描述投影值与预测对象间的非线性关系,并用实码加速遗传算法优化投影指标函数和TR模型参数.实例的计算结果表明,用PPTAR模型预测海洋冰情时序是可行和有效的.PPTAR模型简便、适用性强,克服了目前投影寻踪方法计算量大、编程实现困难的缺点,有助于投影寻踪方法的推广应用,为解决非线性时序复杂预测问题提供了新的途径.

  11. 遗传门限自回归模型在感潮河段水位预测中的应用%Application of genetic threshold auto-regressive model to water stage forecasting for tidal river section

    Institute of Scientific and Technical Information of China (English)

    吴玲莉; 张玮

    2005-01-01

    应用门限自回归(TAR)模型建立了同时受潮汐和径流双重影响的长江下游感潮河段高桥水文站月水位TAR预测模型,建模过程中运用遗传算法来实现模型参数的优化.计算结果显示,门限自回归模型可以拟合感潮河段的非线性特性,拟合及预测精度均满足水文预报规范要求,遗传算法的引入简化了建模过程,提高了模型的预测精度并保证了其预测性能的稳定性.研究结果表明用遗传门限自回归模型预测感潮河段的水位是可行的,该模型在感潮河段其他水文要素的非线性时序预测中也具有广泛的实用价值.

  12. Improvement of Threshold Auto-regressive Model Based on Genetic Algorithm and Its Application%遗传门限自回归模型的改进及其应用

    Institute of Scientific and Technical Information of China (English)

    金光球; 汪莲; 王宗志; 徐金; 曹明宏

    2006-01-01

    针对门限自回归模型在实际应用过程中预测效果差于拟合效果的情况,对门限自回归模型作了改进,即:在对时序x(i)拟合和预测时,AR式靠近i半个周期的观测值用门限自回归模型的拟合和预测的计算值代替;为了清晰直观地确定出延迟步数及门限区间AR模型的阶数,提出了通过绘制自相关系数图来确定.实例表明,该改进方法提高了遗传门限自回归模型的稳定性和实用性,模型在大坝安全位移监测预报中得到了成功的应用.

  13. 门限自回归模型在梅雨降水量预测中的应用%Application of Threshold Auto-Regressive Model to Meiyu Rainfall Forecasting

    Institute of Scientific and Technical Information of China (English)

    卞正奎; 刘文军; 刘方

    2006-01-01

    针对梅雨量的分布特性,提出应用门限自回归模型建立一套简便实用的梅雨量丰枯预测方案.利用相关分析取得恰当的延迟特性,根据梅雨量在X轴上的分布特点选取门限值,并通过改进遗传算法和高斯-牛顿法优化回归系数项和回归系数,最后采用分级预报方法对梅雨量做5级预报.该方案在1967~2002年泰州地区梅雨量丰枯预测中取得了很好的效果,其历史拟合率和预报准确率分别达到87%和80%.

  14. 航空发动机燃烧室声载荷自激励门限自回归仿真模型%Self-Excited Threshold Auto-Regressive Simulation Model of Aeroengine Combustor Acoustic Loading

    Institute of Scientific and Technical Information of China (English)

    吴兆奇; 关蓬莱; 吴晓明

    2009-01-01

    采用自激励门限自回归分析方法,利用航空发动机环形燃烧室测试数据,得到燃烧室噪声声压时间信号自激励门限自回归仿真模型SETAR(2:1;15,14),并与控后非门限的自回归滑动平均模型ARMA(6,5)进行了比较,仿真结果表明,误差方差明显降低.

  15. APPLICATION OF GENETIC THRESHOLD AUTO-REGRESSIVE MODEL TO FORECASTING METEOROLOGICAL TIME SERIES%遗传门限自回归模型在气象时间序列预测中的应用

    Institute of Scientific and Technical Information of China (English)

    金菊良; 杨晓华; 金保明; 丁晶

    2001-01-01

    提出了建立门限自回归模型(TAR)的一套简便通用的方法.用基于实码的改进遗传算法,可同时优化门限值和自回归系数,解决了TAR建模过程所涉及的大量复杂寻优工作这一难题,为TAR模型在气象预测中的广泛应用提供了有力工具.实例计算的结果说明:通过门限值的控制作用,TAR模型可有效地利用气象时序资料所隐含的时序分段相依性这一重要信息,限制了模型误差,保证了TAR模型预测性能的稳健性,提高了预测精度.该方法具有通用性,在各种气象非线性时序预测中具有广泛的实用价值.

  16. 门限自回归模型参数的局部正交设计寻优法%An optimizing method of threshold auto-regressive model parameters in partial orthogonal design

    Institute of Scientific and Technical Information of China (English)

    吴晓明; 关蓬莱

    2003-01-01

    在采用点值图确定门限区间个数的基础上,对门限自回归模型中门限值、滞后步长、各门限区间模型阶数,利用正交设计方法寻优,计算工作量锐减,却可得到精度较高的预报模型.

  17. Self-exciting Threshold Auto-regressive Model for Water Requirement Forecasing of Well Irrigation Rice%基于自激励门限自回归模型的井灌水稻需水量预测

    Institute of Scientific and Technical Information of China (English)

    付强; 潘峰; 金菊良

    2002-01-01

    利用自激励门限自回归模型(SETAR)能够描述具有极限点和极限环的非线性系统及刻画幅频相依等非线性现象的特点,对三江平原井灌水稻各生育阶段需水量的长系列资料进行了分析,建立了SETAR(2,6,3,3)模型,采用9个参数有效地描述了水稻需水量各个生育期在多种气象及其他影响因子的作用下的周期变化的非线性复杂系统.经与实测值相比,精度较高,可在灌区规划管理与优化水稻灌溉制度中应用.

  18. 用于机械故障诊断的门限自回归模型盲辨识%Blind identification of threshold auto-regressive model for machine fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    李志农; 何永勇; 褚福磊; 吴昭同

    2005-01-01

    提出了一种门限自回归(AR)模型的盲辨识算法,并与常用方法进行比较分析.该算法的特点在保证辨识精度上可大大提高其运行速度,而且阶次越高,该算法的优势越明显.将该方法与隐Markov模型结合,以门限自回归模型各区间的AR子模型系数作为特征向量,以隐Markov模型作为分类器,应用到旋转机械升降速过程的故障诊断中.实验结果表明,这种方法有很好的实用性.

  19. Simulation of bridge fluctuating wind field based on Auto-Regressive model%基于自回归模型的桥梁脉动风场模拟

    Institute of Scientific and Technical Information of China (English)

    徐丰; 詹昊; 梁琛

    2015-01-01

    为了客观量化桥梁结构上各点处的脉动风速时程从而为准确计算风荷载效应提供前提保证,在考虑结构节点间的风速时程相关性的基础上,采用线性滤波技术亦即白噪声滤波法中的自回归模型(AR)模拟了某斜拉桥主梁上个别代表点处在任意200 s时长内的风速随机时程.该模型通过线性滤波器将白噪声随机序列输出为符合一定谱特征的平稳随机过程.分别采用Kaimal谱和Panofsky谱作为水平向风和竖向风的风速谱.结果表明:AR模型可以有效模拟脉动风速时程,合成的随机风速时程能够客观反映各代表点风速的时间相关性和空间相关性,其功率谱和目标功率谱也吻合较好.

  20. Application of artificial neural network and adaptive neuro-fuzzy inference system to investigate corrosion rate of zirconium-based nano-ceramic layer on galvanized steel in 3.5% NaCl solution

    Energy Technology Data Exchange (ETDEWEB)

    Mousavifard, S.M. [Department of Polymer Engineering and Color Technology, Amirkabir University of Technology, Tehran (Iran, Islamic Republic of); Attar, M.M., E-mail: attar@aut.ac.ir [Department of Polymer Engineering and Color Technology, Amirkabir University of Technology, Tehran (Iran, Islamic Republic of); Ghanbari, A. [Department of Polymer Engineering and Color Technology, Amirkabir University of Technology, Tehran (Iran, Islamic Republic of); Dadgar, M. [Textile Engineering Department, Neyshabur University, Neyshabur (Iran, Islamic Republic of)

    2015-08-05

    Highlights: • Film formation of Zr-based conversion coating under different conditions was investigated. • We study the effect of some parameters on anticorrosion performance of conversion coating. • Optimization of processing conditions for surface treatment of galvanized steel was obtained. • Modeling and predicting corrosion current density of treated surfaces was performed using ANN and ANFIS. - Abstract: A nano-ceramic Zr-based conversion solution was prepared and optimization of Zr concentration, pH, temperature and immersion time for the treatment of hot-dip galvanized steel (HDG) was performed. SEM microscopy was utilized to investigate the microstructure and film formation of the layer and the anticorrosion performance of conversion coating was studied using polarization test. Artificial intelligence systems (ANN and ANFIS) were applied on the data obtained from polarization test and the models for predicting corrosion current density values were attained. The outcome of these models showed proper predictability of the methods. The influence of input parameters was discussed and the optimized conditions for Zr-based conversion layer formation on the galvanized steel were obtained as follows: pH 3.8–4.5, Zr concentration of about 100 ppm, ambient temperature and immersion time of about 90 s.

  1. Prediction and Optimization Approaches for Modeling and Selection of Optimum Machining Parameters in CNC down Milling Operation

    Directory of Open Access Journals (Sweden)

    Asaad A. Abdullah

    2014-04-01

    Full Text Available In this study, we suggested intelligent approach to predict and optimize the cutting parameters when down milling of 45# steel material with cutting tool PTHK- (Ø10*20C*10D*75L -4F-1.0R under dry condition. The experiments were performed statistically according to four factors with three levels in Taguchi experimental design method. Adaptive Neuro-fuzzy inference system is utilized to establish the relationship between the inputs and output parameter exploiting the Taguchi orthogonal array L27. The Particle Swarm Optimized-Adaptive Neuro-Fuzzy Inference System (PSOANFIS is suggested to select the best cutting parameters providing the lower surface through from the experimental data using ANFIS models to predict objective functions. The PSOANFIS optimization approach that improves the surface quality from 0.212 to 0.202, as well as the cutting time is also reduced from 7.5 to 4.78 sec according to machining parameters before and after optimization process. From these results, it can be readily achieved that the advanced study is trusted and suitable for solving other problems encountered in metal cutting operations and the same surface roughness.

  2. FACE RECOGNITION USING FEATURE EXTRACTION AND NEURO-FUZZY TECHNIQUES

    Directory of Open Access Journals (Sweden)

    Ritesh Vyas

    2012-09-01

    Full Text Available Face is a primary focus of attention in social intercourse, playing a major role in conveying identity and emotion. The human ability to recognize faces is remarkable. People can recognize thousands of faces learned throughout their lifetime and identify familiar faces at a glance even after years of separation. This skill is quite robust, despite large changes in the visual stimulus due to viewing conditions, expression, aging, and distractions such as glasses, beards or changes in hair style. In this work, a system is designed to recognize human faces depending on their facial features. Also to reveal the outline of the face, eyes and nose, edge detection technique has been used. Facial features are extracted in the form of distance between important feature points. After normalization, these feature vectors are learned by artificial neural network and used to recognize facial image.

  3. A neuro-fuzzy architecture for real-time applications

    Science.gov (United States)

    Ramamoorthy, P. A.; Huang, Song

    1992-01-01

    Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach.

  4. Performance Enhancement of Intrusion Detection using Neuro - Fuzzy Intelligent System

    Directory of Open Access Journals (Sweden)

    Dr. K. S. Anil Kumar

    2014-10-01

    Full Text Available This research work aims at developing hybrid algorithms using data mining techniques for the effective enhancement of anomaly intrusion detection performance. Many proposed algorithms have not addressed their reliability with varying amount of malicious activity or their adaptability for real time use. The study incorporates a theoretical basis for improvement in performance of IDS using K-medoids Algorithm, Fuzzy Set Algorithm, Fuzzy Rule System and Neural Network techniques. Also statistical significance of estimates has been looked into for finalizing the best one using DARPA network traffic datasets.

  5. Skin Cancer Recognition by Using a Neuro-Fuzzy System

    OpenAIRE

    Bareqa Salah; Mohammad Alshraideh; Rasha Beidas; Ferial Hayajneh

    2011-01-01

    Skin cancer is the most prevalent cancer in the light-skinned population and it is generally caused by exposure to ultraviolet light. Early detection of skin cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose skin cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the clinician. To obviate these problems, image processing techniques, a neural network system (NN) ...

  6. Classification of Sleep Stages in Infants: A Neuro Fuzzy Approach

    Science.gov (United States)

    2007-11-02

    detect sigma spindles (SS), which are in the 12-14 Hz range. The electrooculogram (EOG) and the electromyogram ( EMG ) are used to determine the presence of...from the international 10-20 system (FP1-C3, C3-O1, FP2-C4, C4-O2, and C3-C4); EOG for REMov detection; tonic chin and diaphragmatic EMGs ; ECG; body...movement detection of upper and lower limbs using piezo-electric crystal transducers ; abdominal ventilatory movements, using a mercury strain gauge; and

  7. Characterizing root distribution with adaptive neuro-fuzzy analysis

    Science.gov (United States)

    Root-soil relationships are pivotal to understanding crop growth and function in a changing environment. Plant root systems are difficult to measure and remain understudied relative to above ground responses. High variation among field samples often leads to non-significance when standard statistics...

  8. Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity

    Science.gov (United States)

    Leite, Argentina; Paula Rocha, Ana; Eduarda Silva, Maria

    2013-06-01

    Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation.

  9. Hydrograph estimation with fuzzy chain model

    Science.gov (United States)

    Güçlü, Yavuz Selim; Şen, Zekai

    2016-07-01

    Hydrograph peak discharge estimation is gaining more significance with unprecedented urbanization developments. Most of the existing models do not yield reliable peak discharge estimations for small basins although they provide acceptable results for medium and large ones. In this study, fuzzy chain model (FCM) is suggested by considering the necessary adjustments based on some measurements over a small basin, Ayamama basin, within Istanbul City, Turkey. FCM is based on Mamdani and the Adaptive Neuro Fuzzy Inference Systems (ANFIS) methodologies, which yield peak discharge estimation. The suggested model is compared with two well-known approaches, namely, Soil Conservation Service (SCS)-Snyder and SCS-Clark methodologies. In all the methods, the hydrographs are obtained through the use of dimensionless unit hydrograph concept. After the necessary modeling, computation, verification and adaptation stages comparatively better hydrographs are obtained by FCM. The mean square error for the FCM is many folds smaller than the other methodologies, which proves outperformance of the suggested methodology.

  10. System Identification of Civil Engineering Structures using State Space and ARMAV Models

    DEFF Research Database (Denmark)

    Andersen, P.; Kirkegaard, Poul Henning; Brincker, Rune

    In this paper the relations between an ambient excited structural system, represented by an innovation state space system, and the Auto-Regressive Moving Average Vector (ARMAV) model are considered. It is shown how to obtain a multivariate estimate of the ARMAV model from output measurements, usi...

  11. Identification of Civil Engineering Structures using Multivariate ARMAV and RARMAV Models

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune

    This paper presents how to make system identification of civil engineering structures using multivariate auto-regressive moving-average vector (ARMAV) models. Further, the ARMAV technique is extended to a recursive technique (RARMAV). The ARMAV model is used to identify measured stationary data....... The results show the usefulness of the approaches for identification of civil engineering structures excited by natural excitation...

  12. Modelling Croatian Export Dynamics Using Global Macroeconometric Model

    OpenAIRE

    Jakšić, Saša; Žmuk, Berislav

    2014-01-01

    Five years following the occurrence of the global economic and financial crisis, Croatia is one of the few countries in the region whose export has still not recovered to the pre-crisis level. In order to properly account for international linkages and possible crisis spillover effects, a Global Vector AutoRegressive (GVAR) model is defined. The GVAR model is a consistent global macroeconometric model which enables modelling interactions between Croatia and a set of Central and Southeast Euro...

  13. Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs)

    Science.gov (United States)

    Roushangar, Kiyoumars; Mehrabani, Fatemeh Vojoudi; Shiri, Jalal

    2014-06-01

    This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. Gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting.

  14. Wind turbine condition monitoring based on SCADA data using normal behavior models

    DEFF Research Database (Denmark)

    Schlechtingen, Meik; Santos, Ilmar; Achiche, Sofiane

    2013-01-01

    This paper proposes a system for wind turbine condition monitoring using Adaptive Neuro-Fuzzy Interference Systems (ANFIS). For this purpose: (1) ANFIS normal behavior models for common Supervisory Control And Data Acquisition (SCADA) data are developed in order to detect abnormal behavior....... The applicability of the set up ANFIS models for anomaly detection is proved by the achieved performance of the models. In combination with the FIS the prediction errors can provide information about the condition of the monitored components. In this paper the condition monitoring system is described. Part two...... the applicability of ANFIS models for monitoring wind turbine SCADA signals. The computational time needed for model training is compared to Neural Network (NN) models showing the strength of ANFIS in training speed. (2) For automation of fault diagnosis Fuzzy Interference Systems (FIS) are used to analyze...

  15. Rock mechanics modeling based on soft granulation theory

    CERN Document Server

    Owladeghaffari, H

    2008-01-01

    This paper describes application of information granulation theory, on the design of rock engineering flowcharts. Firstly, an overall flowchart, based on information granulation theory has been highlighted. Information granulation theory, in crisp (non-fuzzy) or fuzzy format, can take into account engineering experiences (especially in fuzzy shape-incomplete information or superfluous), or engineering judgments, in each step of designing procedure, while the suitable instruments modeling are employed. In this manner and to extension of soft modeling instruments, using three combinations of Self Organizing Map (SOM), Neuro-Fuzzy Inference System (NFIS), and Rough Set Theory (RST) crisp and fuzzy granules, from monitored data sets are obtained. The main underlined core of our algorithms are balancing of crisp(rough or non-fuzzy) granules and sub fuzzy granules, within non fuzzy information (initial granulation) upon the open-close iterations. Using different criteria on balancing best granules (information pock...

  16. Comparison of different modelling approaches of drive train temperature for the purposes of wind turbine failure detection

    Science.gov (United States)

    Tautz-Weinert, J.; Watson, S. J.

    2016-09-01

    Effective condition monitoring techniques for wind turbines are needed to improve maintenance processes and reduce operational costs. Normal behaviour modelling of temperatures with information from other sensors can help to detect wear processes in drive trains. In a case study, modelling of bearing and generator temperatures is investigated with operational data from the SCADA systems of more than 100 turbines. The focus is here on automated training and testing on a farm level to enable an on-line system, which will detect failures without human interpretation. Modelling based on linear combinations, artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines and Gaussian process regression is compared. The selection of suitable modelling inputs is discussed with cross-correlation analyses and a sensitivity study, which reveals that the investigated modelling techniques react in different ways to an increased number of inputs. The case study highlights advantages of modelling with linear combinations and artificial neural networks in a feedforward configuration.

  17. Developing of New Facets of Indirect Modeling in the Geosciences

    CERN Document Server

    Owladeghaffari, Hamed; Sharifzadeh, Mostafa

    2008-01-01

    In this paper, we describe some applications of Self Organizing feature map Neuro-Fuzzy Inference System (SONFIS) and Self Organizing feature map Rough Set (SORST) in analysis of permeability at a dam site and lost circulation in the drilling of three wells in Iran. Elicitation of the best rules on the information tables, exploration of the dominant structures on the behaviour of systems while they fall in to the balance of the second granulation level (rules) and highlighting of most effective attributes (parameters) on the selected systems, are some of the benefits of the proposed methods. In the other process, using complex networks (graphs) theory - as another method in not 1:1 modelling branch- mechanical behaviour of a rock joint has been investigated. Keywords: Information Granules; SONFIS; SORST; Complex Networks; Permeability; Lost Circulation; Mechanical Behavior of a Rock Joint

  18. ARMA-GM combined forewarning model for the quality control

    Institute of Scientific and Technical Information of China (English)

    Wang Xingyuan; Yang Xu

    2005-01-01

    Three forecasting models are set up: the auto-regressive moving average model, the grey forecasting model for the rate of qualified products Pt, and the grey forecasting model for time intervals of the quality catastrophes. Then a combined forewarning system for the quality of products is established, which contains three models, judgment rules and forewarning state illustration. Finally with an example of the practical production, this modeling system is proved fairly effective.

  19. Model structure selection in convolutive mixtures

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai

    2006-01-01

    The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimoneous represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimoneous...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: 'Are we actually dealing with a convolutive mixture?'. We try to answer this question for EEG data....

  20. Model structure selection in convolutive mixtures

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai

    2006-01-01

    The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....

  1. Nonlinear inverse modeling of sensor based on back-propagation fuzzy logical system

    Institute of Scientific and Technical Information of China (English)

    Li Jun; Liu Junhua

    2007-01-01

    Objective To correct the nonlinear error of sensor output, a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System (BP FS) is presented. Methods The BP FS is a computationally efficient nonlinear universal approximator, which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed. Results The neuro-fuzzy hybrid system, i.e. BP FS, is then applied to construct nonlinear inverse model of pressure sensor. The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation, and thus the performance of pressure sensor is significantly improved. Conclusion The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.

  2. Parametric optimization of friction stir welding process of age hardenable aluminum alloys-ANFIS modeling

    Institute of Scientific and Technical Information of China (English)

    D Vijayan; V Seshagiri Rao

    2016-01-01

    A comparative approach was performed between the response surface method (RSM) and the adaptive neuro-fuzzy inference system (ANFIS) to enhance the tensile properties, including the ultimate tensile strength and the tensile elongation, of friction stir welded age hardenable AA6061 and AA2024 aluminum alloys. The effects of the welding parameters, namely the tool rotational speed, welding speed, axial load and pin profile, on the ultimate tensile strength and the tensile elongation were analyzed using a three-level, four-factor Box-Behnken experimental design. The developed design was utilized to train the ANFIS models. The predictive capabilities of RSM and ANFIS were compared based on the root mean square error, the mean absolute error, and the correlation coefficient based on the obtained data set. The results demonstrate that the developed ANFIS models are more effective than the RSM model.

  3. Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling

    Science.gov (United States)

    Drigas, Athanasios S.; Argyri, Katerina; Vrettaros, John

    Artificial Intelligence applications in educational field are getting more and more popular during the last decade (1999-2009) and that is why much relevant research has been conducted. In this paper, we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro - fuzzy systems and genetic programming neural networks (GPNN) in student modeling. This latest research trend is a part of every Intelligent Tutoring System and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used to point out their qualities and then we attempt a navigation to the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.

  4. Techno-economical optimization of Reactive Blue 19 removal by combined electrocoagulation/coagulation process through MOPSO using RSM and ANFIS models.

    Science.gov (United States)

    Taheri, M; Alavi Moghaddam, M R; Arami, M

    2013-10-15

    In this research, Response Surface Methodology (RSM) and Adaptive Neuro Fuzzy Inference System (ANFIS) models were applied for optimization of Reactive Blue 19 removal using combined electrocoagulation/coagulation process through Multi-Objective Particle Swarm Optimization (MOPSO). By applying RSM, the effects of five independent parameters including applied current, reaction time, initial dye concentration, initial pH and dosage of Poly Aluminum Chloride were studied. According to the RSM results, all the independent parameters are equally important in dye removal efficiency. In addition, ANFIS was applied for dye removal efficiency and operating costs modeling. High R(2) values (≥85%) indicate that the predictions of RSM and ANFIS models are acceptable for both responses. ANFIS was also used in MOPSO for finding the best techno-economical Reactive Blue 19 elimination conditions according to RSM design. Through MOPSO and the selected ANFIS model, Minimum and maximum values of 58.27% and 99.67% dye removal efficiencies were obtained, respectively.

  5. 基于模糊神经模型的电厂协调预测控制%Constrained Power Plant Coordinated Predictive Control Using Neurofuzzy Model

    Institute of Scientific and Technical Information of China (English)

    刘向杰; 刘吉臻

    2006-01-01

    In unit steam-boiler generation, a coordinated control strategy is required to ensure a higher rate of load change without violating thermal constraints. The process is characterized by nonlinearity and uncertainty. While neural networks can model highly complex nonlinear dynamical systems, they produce black box models. This has led to significant interest in neuro-fuzzy networks (NFNs) to represent a nonlinear dynamical process by a set of locally valid and simpler submodels. Two alternative methods of exploiting the NFNs within a generalised predictive control (GPC)framework for nonlinear model predictive control are described. Coordinated control of steam-boiler generation using the two nonlinear GPC methods show excellent tracking and disturbance rejection results and improved performance compared with conventional linear GPC.

  6. Validated dynamic flow model

    DEFF Research Database (Denmark)

    Knudsen, Torben

    2011-01-01

    The purpose with this deliverable 2.5 is to use fresh experimental data for validation and selection of a flow model to be used for control design in WP3-4. Initially the idea was to investigate the models developed in WP2. However, in the project it was agreed to include and focus on a additive...... model turns out not to be useful for prediction of the flow. Moreover, standard Box Jenkins model structures and multiple output auto regressive models proves to be superior as they can give useful predictions of the flow....

  7. BLIND AND COMPLETE MODELING OF LINEAR SYSTEMS USING THIRD ORDER CUMULANTS

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    This paper presents a novel approach to structure determination of linear systems along with the choice of system orders and parameters. AutoRegressive (AR), Moving Average (MA) or AutoRegressive-Moving Average (ARMA) model structure can be extracted blindly from the Third Order Cumulants (TOC) of the system output measurements, where the unknown system is driven by an unobservable stationary independent identically distributed (i.i.d.) non-Gaussian signal. By means of the system order recursion, whether the system has an AR structure or has AR part of an ARMA structure is firstly investigated. MA features in the TOC domain is then applied as a threshold to decide if the system is an MA model or has MA part of an ARMA model. Numerical simulations illustrate the generality of the proposed blind structure identification methodology that may serve as a guideline for blind linear system modeling.

  8. PENGENDALIAN ASET NIRWUJUD DALAM MANAJEMEN SISTEM IRIGASI: KONSEP DAN PENGEMBANGAN MODEL (Controlling Intangible Assets in Irrigation System Management:Concept and Model Development

    Directory of Open Access Journals (Sweden)

    Nugroho Tri Waskitho

    2013-06-01

    Full Text Available Irrigation was an important component of the agricultural development in Indonesia, but it had many problems. Irrigation management was inefficient, irrigation networks were damaged and farmers participation were poor. These problems were caused by poor of intangible assets. The research aimed at developing the concept and the model of controlling intangible assets in irrigation system management. The research method consisted of two stages. The first stage was developing the concept. The concept of controlling intangible assets in irrigation system management was developed based on principles of knowledge management. The concept stated that intangible assets in irrigation system can be controlled using knowledge management. The second stage was developing the model which consisted of model building and sensivity analysis. Model of controlling intangible assets in irrigation system management was build using neuro-fuzzy. The model had three submodels: knowledge management, intangible assets and performance of irrigation system. Evaluating the model was done in Sapon irrigation system in Kulon Progo, Yogyakarta. Data collecting was done using questionnaire on nine Water Use Associations. Data analysis was done using Adaptive Neuro Fuzzy Inference System. The model had been evaluated using correlation coefficient, Mean Absolute Percentage Error and Root Mean Square Error. Result of the study indicated that the concept of controlling intangible assets in irrigation system management had developed based on knowledge management. The concept stated that irrigation system management had to balance between tangible assets and intangible assets. Intangible assets which had amortization need be controlled. Controlling intangible assets can be done by knowledge management. The model of controlling intangible assets in irrigation system management could predict intangible assets and performance of irrigation system well. The model linked knowledge

  9. THEORETICAL MODEL AND NUMERICAL METHOD ON ONLINE IDENTIFICATION OF DYNAMICAL CHARACTERISTICS OF STRUCTURAL SYSTEM

    Institute of Scientific and Technical Information of China (English)

    姚志远; 汪凤泉

    2004-01-01

    An online method of identification of dynamic characteristics only using measured ambient response of structural dynamic system is widely focused on. The Ibrahim and ARMA (AutoRegressive Moving Average ) methods are basic identification methods. A model on dynamic system suffered by random ambient excitation was researched into, and a subspace decomposition method being different from traditional harmonic retrieval method was introduced. Robustness and effectiveness of this approach on identification of vibration characteristics are demonstrated on numerical experiment.

  10. Runoff prediction using an integrated hybrid modelling scheme

    Science.gov (United States)

    Remesan, Renji; Shamim, Muhammad Ali; Han, Dawei; Mathew, Jimson

    2009-06-01

    SummaryRainfall runoff is a very complicated process due to its nonlinear and multidimensional dynamics, and hence difficult to model. There are several options for a modeller to consider, for example: the type of input data to be used, the length of model calibration (training) data and whether or not the input data be treated as signals with different frequency bands so that they can be modelled separately. This paper describes a new hybrid modelling scheme to answer the above mentioned questions. The proposed methodology is based on a hybrid model integrating wavelet transformation, a modelling engine (Artificial Neural Network) and the Gamma Test. First, the Gamma Test is used to decide the required input data dimensions and its length. Second, the wavelet transformation decomposes the input signals into different frequency bands. Finally, a modelling engine (ANN in this study) is used to model the decomposed signals separately. The proposed scheme was tested using the Brue catchment, Southwest England, as a case study and has produced very positive results. The hybrid model outperforms all other models tested. This study has a wider implication in the hydrological modelling field since its general framework could be applied to other model combinations (e.g., model engine could be Support Vector Machines, neuro-fuzzy systems, or even a conceptual model. The signal decomposition could be carried out by Fourier transformation).

  11. A geomorphology-based ANFIS model for multi-station modeling of rainfall-runoff process

    Science.gov (United States)

    Nourani, Vahid; Komasi, Mehdi

    2013-05-01

    This paper demonstrates the potential use of Artificial Intelligence (AI) techniques for predicting daily runoff at multiple gauging stations. Uncertainty and complexity of the rainfall-runoff process due to its variability in space and time in one hand and lack of historical data on the other hand, cause difficulties in the spatiotemporal modeling of the process. In this paper, an Integrated Geomorphological Adaptive Neuro-Fuzzy Inference System (IGANFIS) model conjugated with C-means clustering algorithm was used for rainfall-runoff modeling at multiple stations of the Eel River watershed, California. The proposed model could be used for predicting runoff in the stations with lack of data or any sub-basin within the watershed because of employing the spatial and temporal variables of the sub-basins as the model inputs. This ability of the integrated model for spatiotemporal modeling of the process was examined through the cross validation technique for a station. In this way, different ANFIS structures were trained using Sugeno algorithm in order to estimate daily discharge values at different stations. In order to improve the model efficiency, the input data were then classified into some clusters by the means of fuzzy C-means (FCMs) method. The goodness-of-fit measures support the gainful use of the IGANFIS and FCM methods in spatiotemporal modeling of hydrological processes.

  12. Comparison of Artificial Intelligence Techniques for river flow forecasting

    Directory of Open Access Journals (Sweden)

    M. Firat

    2008-01-01

    Full Text Available The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS and Artificial Neural Network (ANN methods, Generalized Regression Neural Networks (GRNN and Feed Forward Neural Networks (FFNN, and Auto-Regressive (AR models for forecasting of daily river flow is investigated and Seyhan River and Cine River was chosen as case study area. For the Seyhan River, the forecasting models are established using combinations of antecedent daily river flow records. On the other hand, for the Cine River, daily river flow and rainfall records are used in input layer. For both stations, the data sets are divided into three subsets, training, testing and verification data set. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN and AR methods. The results of all models for both training and testing are evaluated and the best fit input structures and methods for both stations are determined according to criteria of performance evaluation. Moreover the best fit forecasting models are also verified by verification set which was not used in training and testing processes and compared according to criteria. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily river flow forecasting.

  13. Macrocell path loss prediction using artificial intelligence techniques

    Science.gov (United States)

    Usman, Abraham U.; Okereke, Okpo U.; Omizegba, Elijah E.

    2014-04-01

    The prediction of propagation loss is a practical non-linear function approximation problem which linear regression or auto-regression models are limited in their ability to handle. However, some computational Intelligence techniques such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs) have been shown to have great ability to handle non-linear function approximation and prediction problems. In this study, the multiple layer perceptron neural network (MLP-NN), radial basis function neural network (RBF-NN) and an ANFIS network were trained using actual signal strength measurement taken at certain suburban areas of Bauchi metropolis, Nigeria. The trained networks were then used to predict propagation losses at the stated areas under differing conditions. The predictions were compared with the prediction accuracy of the popular Hata model. It was observed that ANFIS model gave a better fit in all cases having higher R2 values in each case and on average is more robust than MLP and RBF models as it generalises better to a different data.

  14. COMPUTATIONALLY INTELLIGENT MODELLING AND CONTROL OF FLUIDIZED BED COMBUSTION PROCESS

    Directory of Open Access Journals (Sweden)

    Ivan T Ćirić

    2011-01-01

    Full Text Available In this paper modelling and control approaches for fluidized bed combustion process have been considered, that are based on the use of computational intelligence. Proposed adaptive neuro-fuzzy-genetic modelling and intelligent control strategies provide for efficient combining of available expert knowledge with experimental data. Firstly, based on the qualitative information on the desulphurization process, models of the SO2 emission in fluidized bed combustion have been developed, which provides for economical and efficient reduction of SO2 in FBC by estimation of optimal process parameters and by design of intelligent control systems based on defined emission models. Also, efficient fuzzy nonlinear FBC process modelling strategy by combining several linearized combustion models has been presented. Finally, fuzzy and conventional process control systems for fuel flow and primary air flow regulation based on developed models and optimized by genetic algorithms have also been developed. Obtained results indicate that computationally intelligent approach can be successfully applied for modelling and control of complex fluidized bed combustion process.

  15. 非线性时间序列门限自回归模型在环境空气质量预报中的应用%An Application of Threshold Auto-Regressive Model to Predicting Ambient Air Quality

    Institute of Scientific and Technical Information of China (English)

    潘磊; 沙斐

    2007-01-01

    门限自回归模型作为1种利用历史资料对非线性时间序列进行描述从而进行预测的数学模型,多年来在各种统计领域中均取得良好的效果.根据非线性时间序列的门限自回归模型(简称TAR)的基本思路,利用环境空气自动监测系统历史监测数据资料,建立了浦东新区环境空气质量的预报计算模型.通过统计检验,检查使用TAR模型预测浦东新区环境空气质量与实际监测情况的符合程度.讨论了该模型在监测工作中的应用可行性.

  16. 基于遗传算法的门限自回归模型在浅层地下水位预测中的应用%Threshold auto-regressive model based on genetic algorithm and its application to forecasting the shallow groundwater level

    Institute of Scientific and Technical Information of China (English)

    金菊良; 丁晶; 魏一鸣

    1999-01-01

    提出了建立门限自回归模型的一种简便通用的方法,用遗传算法可同时优化门限值和自回归系数.实例计算的结果说明了其可行性和有效性.该方法在各种工程时序预测中具有广泛的应用价值.

  17. SELF TUNING CONTROLLERS FOR DAMPING LOW FREQUENCY OSCILLATIONS

    Directory of Open Access Journals (Sweden)

    SANGU RAVINDRA

    2012-09-01

    Full Text Available This paper presents a new control methods based on adaptive Neuro-Fuzzy damping controller and adaptive Artificial Neural Networks damping controller techniques to control a Unified Power Flow controller (UPFC installed in a single machine infinite bus Power System. The objective of Neuro-Fuzzy and ANN based UPFC controller is to damp power system oscillations.Phillips-Herffron model of a single machine power system equipped with a UPFC is used to model the system. In order to damp power system oscillations, adaptive neuro-fuzzy damping controller and adaptive ANN damping controller for UPFC are designed and simulated. Simulation is performed for various types of loads and for different disturbances. Simulation results demonstrate that the developed adaptive ANN damping controller has an excellent capability in damping electromechanical oscillations which exhibits a superior damping performance in comparison to the neuro-fuzzy damping controller as well as conventional lead-lag controller.

  18. Efficiency improvement in multi-sensor wireless network based estimation algorithms for distributed parameter systems with application at the heat transfer

    Science.gov (United States)

    Volosencu, Constantin; Curiac, Daniel-Ioan

    2013-12-01

    This paper gives a technical solution to improve the efficiency in multi-sensor wireless network based estimation for distributed parameter systems. A complex structure based on some estimation algorithms, with regression and autoregression, implemented using linear estimators, neural estimators and ANFIS estimators, is developed for this purpose. The three kinds of estimators are working with precision on different parts of the phenomenon characteristic. A comparative study of three methods - linear and nonlinear based on neural networks and adaptive neuro-fuzzy inference system - to implement these algorithms is made. The intelligent wireless sensor networks are taken in consideration as an efficient tool for measurement, data acquisition and communication. They are seen as a "distributed sensor", placed in the desired positions in the measuring field. The algorithms are based on regression using values from adjacent and also on auto-regression using past values from the same sensor. A modelling and simulation for a case study is presented. The quality of estimation is validated using a quadratic criterion. A practical implementation is made using virtual instrumentation. Applications of this complex estimation system are in fault detection and diagnosis of distributed parameter systems and discovery of malicious nodes in wireless sensor networks.

  19. Design and Implementation of Neuro-Fuzzy Controller Using FPGA for Sun Tracking System

    OpenAIRE

    2016-01-01

    Nowadays, renewable energy is being used increasingly because of the global warming and destruction of the environment. Therefore, the studies are concentrating on gain of maximum power from this energy such as the solar energy. A sun tracker is device which rotates a photovoltaic (PV) panel to the sun to get the maximum power. Disturbances which are originated by passing the clouds are one of great challenges in design of the controller in addition to the losses power due to energy consumpti...

  20. A robust neuro-fuzzy classifier for the detection of cardiomegaly in digital chest radiographies

    Directory of Open Access Journals (Sweden)

    Fabián Torres-Robles

    2014-01-01

    Full Text Available Presentamos un nuevo procedimiento que determina de forma automática y fiable la presencia de cardiomegalia en radiografías torácicas. El CTR muestra la relación entre el tamaño del corazón y el tamaño del tórax. El esquema propuesto utiliza un clasificador robusto difuso para encontrar los valores correctos del tamaño del tórax y los límites del corazón derecho e izquierdo para medir el agrandamiento del corazón para detectar cardiomegalia. El método propuesto utiliza operaciones clásicas de morfología para segmentar los pulmones proporcionando baja complejidad computacional y el método difuso propuesto es robusto para encontrar las medidas correctas del CTR proporcionando un cálculo rápido porque las reglas difusas usan operaciones aritméticas elementales para desempeñar una buena detección de cardiomegalia. Finalmente, se mejoran los resultados de clasificación del método difuso propuesto utilizando una red neuronal función de base radial (RBF en términos de precisión, sensibilidad y especificidad.

  1. Adaptive Critic Based Neuro-Fuzzy Tracker for Improving Conversion Efficiency in PV Solar Cells

    Directory of Open Access Journals (Sweden)

    Halimeh Rashidi

    2012-08-01

    Full Text Available The output power of photovoltaic systems is directly related to the amount of solar energy collected by the system and it is therefore necessary to track the sun’s position with high accuracy. This study proposes multi-agent adaptive critic based nero fuzzy solar tracking system dedicated to PV panels. The proposed tracker ensures the optimal conversion of solar energy into electricity by properly adjusting the PV panels according to the position of the sun. To evaluate the usefulness of the proposed method, some computer simulations are performed and compared with fuzzy PD controller. Obtained results show the proposed control strategy is very robust, flexible and could be used to get the desired performance levels. The response time is also very fast. Simulation results that have been compared with fuzzy PD controller show that our method has the better control performance than fuzzy PD controller.

  2. User/Tutor Optimal Learning Path in E-Learning Using Comprehensive Neuro-Fuzzy Approach

    Science.gov (United States)

    Fazlollahtabar, Hamed; Mahdavi, Iraj

    2009-01-01

    Internet evolution has affected all industrial, commercial, and especially learning activities in the new context of e-learning. Due to cost, time, or flexibility e-learning has been adopted by participators as an alternative training method. By development of computer-based devices and new methods of teaching, e-learning has emerged. The…

  3. Inverse Kinematics Using Neuro-Fuzzy Intelligent Technique for Robotic Manipulator

    Directory of Open Access Journals (Sweden)

    Shiv Manjaree

    2013-12-01

    Full Text Available Inverse Kinematics of robotic manipulators is a complex task. For higher degree of freedom robotic manipulators, the algebra related to traditional approaches become highly complex. This has led to the usage of artificial intelligence techniques. In this paper, the hybrid combination of Neural Networks and Fuzzy Logic Intelligent Technique has been applied for 3 degree of freedom robotic manipulator. The variations of joint angles obtained in the results show the effective implementation of artificial intelligence.

  4. Parameter optimization for intelligent phishing detection using Adaptive Neuro-Fuzzy

    Directory of Open Access Journals (Sweden)

    P. A. Barraclough

    2014-10-01

    Full Text Available Phishing attacks has been growing rapidly in the past few years. As a result, a number of approaches have been proposed to address the problem. Despite various approaches proposed such as feature-based and blacklist-based via machine learning techniques, there is still a lack of accuracy and real-time solution. Most approaches applying machine learning techniques requires that parameters are tuned to solve a problem, but parameters are difficult to tune to a desirable output. This study presents a parameter tuning framework, using adaptive Neuron-fuzzy inference system with comprehensive data to maximize systems performance. Extensive experiment was conducted. During ten-fold cross-validation, the data is split into training and testing pairs and parameters are set according to desirable output and have achieved 98.74% accuracy. Our results demonstrated higher performance compared to other results in the field. This paper contributes new comprehensive data, novel parameter tuning method and applied a new algorithm in a new field. The implication is that adaptive neuron-fuzzy system with effective data and proper parameter tuning can enhance system performance. The outcome will provide a new knowledge in the field.

  5. Use of an adaptive neuro-fuzzy system to characterize root distribution patterns

    Science.gov (United States)

    Root-soil relationships are pivotal to understanding crop growth and function in a changing environmental. Plant root systems are difficult to measure and remain understudied relative to above ground responses. High variation among field samples often leads to non-significance when standard statist...

  6. Obstacle Avoidance of mobile robot using PSO based Neuro Fuzzy Technique

    Directory of Open Access Journals (Sweden)

    Sourav Dutta

    2010-03-01

    Full Text Available Navigation and obstacle avoidance are veryimportant issues for the successful use of an autonomous mobilerobot. To allow the robot to move between its current and finalconfigurations without any collision within the surroundingenvironment, motion planning needs much treatment. Thus togenerate collision free path it should have proper motionplanning as well as obstacle avoidance scheme. This work mainlydeals with the obstacle avoidance of a wheeled mobile robot instructured environment by using PSO based neuro-fuzzyapproach. Here three layer neural network with PSO is used aslearning algorithm to determine the optimal collision-free path.

  7. FACTS Devices Using Neuro Fuzzy Controller in Stabilization of Grid Connected Wind Generator.

    Directory of Open Access Journals (Sweden)

    ROHI KACHROO

    2012-05-01

    Full Text Available Wind power is one of the renewable energy sources. It has various advantages like, cost competitiveness, environmentally clean and safeness. Large wind farms have stability problems when they are integrated to the power system. A thorough analysis is required to identify the stability problems and to develop measures to improve it. Mostly used wind generator is a fixed speed induction generator, which requires reactive power to maintain air gap flux. Reactive ower equipments are used to enable recovery of large wind farms from severe system disturbances. In this paper shunt and series FACTS evices, Static Synchronous Compensator (STATCOM and Static ynchronous Series Compensator are used for the purpose of stabilizing grid connected wind generator against the grid-side disturbances. The essential feature of the FACTS devices is their ability to absorb or inject the reactive power. Since stability is a non linear process so system performance can be improved by using nonlinear controllers. Neurofuzzy controller (NFC is a non linear controller. NFC has fasterresponse than conventional PI controllers

  8. Edificio project: A neuro-fuzzy approach to building energy management systems

    NARCIS (Netherlands)

    Galata, A.; Bakker, L.G.; Morel, N.; Michel, J.B.; Karki, S.; Joergl, H.P.; Franceschini, A.; Martinez, A.

    1998-01-01

    It is well known that building installations for indoor climate control, consume a substantial part of the total energy consumption and that at present these installations use much more energy than required due to inadequate settings and poor control and management strategies. European building ener

  9. Enhanced dynamic Performance of Matrix Converter Cage Drive with Neuro-fuzzy approach

    Directory of Open Access Journals (Sweden)

    R.R. Joshi

    2007-06-01

    Full Text Available This paper proposes a new control algorithm for a matrix converter (MC induction motor drive system. First, a new switching strategy, which applies a back-propagation neural network to adjust a pseudo dc bus voltage, is proposed to reduce the current harmonics of the induction motor. Next, a two-degree-of-freedom controller is proposed to improve the system performance. The controller design algorithm can be applied in an adjustable speed control system and a position control system to obtain good transient responses and good load disturbance rejection abilities. The implementation of this kind of controller is only possible by using a high-speed digital signal processor. In this paper, all the control loops, including current-loop, speed-loop, and position-loop, are implemented by TMS320C6711 digital signal processor. Several experimental results are shown to validate the theoretical analysis.

  10. Neuro-fuzzy control synthesis for hydrostatic type servoactuators. Experimental results

    Directory of Open Access Journals (Sweden)

    Felicia URSU

    2009-12-01

    Full Text Available Continuing recent works of the authors, the paper shows the developing and the application of aneuro-fuzzy control law to the positioning outer loop of a hydrostatic type servoactuator.Experimental results are presented concerning dynamical behavior of the system by using this“intelligent” controller. Finally, arguments about the advantages of the new designed controller aresummarized.

  11. Mathematical modeling of wastewater-derived biodegradable dissolved organic nitrogen.

    Science.gov (United States)

    Simsek, Halis

    2016-11-01

    Wastewater-derived dissolved organic nitrogen (DON) typically constitutes the majority of total dissolved nitrogen (TDN) discharged to surface waters from advanced wastewater treatment plants (WWTPs). When considering the stringent regulations on nitrogen discharge limits in sensitive receiving waters, DON becomes problematic and needs to be reduced. Biodegradable DON (BDON) is a portion of DON that is biologically degradable by bacteria when the optimum environmental conditions are met. BDON in a two-stage trickling filter WWTP was estimated using artificial intelligence techniques, such as adaptive neuro-fuzzy inference systems, multilayer perceptron, radial basis neural networks (RBNN), and generalized regression neural networks. Nitrite, nitrate, ammonium, TDN, and DON data were used as input neurons. Wastewater samples were collected from four different locations in the plant. Model performances were evaluated using root mean square error, mean absolute error, mean bias error, and coefficient of determination statistics. Modeling results showed that the R(2) values were higher than 0.85 in all four models for all wastewater samples, except only R(2) in the final effluent sample for RBNN modeling was low (0.52). Overall, it was found that all four computing techniques could be employed successfully to predict BDON.

  12. A modified dynamic evolving neural-fuzzy approach to modeling customer satisfaction for affective design.

    Science.gov (United States)

    Kwong, C K; Fung, K Y; Jiang, Huimin; Chan, K Y; Siu, Kin Wai Michael

    2013-01-01

    Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.

  13. Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

    Science.gov (United States)

    Abbasi, Maryam; El Hanandeh, Ali

    2016-10-01

    Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg.

  14. Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

    Science.gov (United States)

    Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

    2015-10-01

    Reliable river flow forecasts play a key role in flood risk mitigation. Among different approaches of river flow forecasting, data driven approaches have become increasingly popular in recent years due to their minimum information requirements and ability to simulate nonlinear and non-stationary characteristics of hydrological processes. In this study, attempts are made to apply four different types of data driven approaches, namely traditional artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), wavelet neural networks (WNN), and, hybrid ANFIS with multi resolution analysis using wavelets (WNF). Developed models applied for real time flood forecasting at Casino station on Richmond River, Australia which is highly prone to flooding. Hourly rainfall and runoff data were used to drive the models which have been used for forecasting with 1, 6, 12, 24, 36 and 48 h lead-time. The performance of models further improved by adding an upstream river flow data (Wiangaree station), as another effective input. All models perform satisfactorily up to 12 h lead-time. However, the hybrid wavelet-based models significantly outperforming the ANFIS and ANN models in the longer lead-time forecasting. The results confirm the robustness of the proposed structure of the hybrid models for real time runoff forecasting in the study area.

  15. Computing Air Demand Using the Takagi–Sugeno Model for Dam Outlets

    Directory of Open Access Journals (Sweden)

    Mohammad Zounemat-Kermani

    2013-09-01

    Full Text Available An adaptive neuro-fuzzy inference system (ANFIS was developed using the subtractive clustering technique to study the air demand in low-level outlet works. The ANFIS model was employed to calculate vent air discharge in different gate openings for an embankment dam. A hybrid learning algorithm obtained from combining back-propagation and least square estimate was adopted to identify linear and non-linear parameters in the ANFIS model. Empirical relationships based on the experimental information obtained from physical models were applied to 108 experimental data points to obtain more reliable evaluations. The feed-forward Levenberg-Marquardt neural network (LMNN and multiple linear regression (MLR models were also built using the same data to compare model performances with each other. The results indicated that the fuzzy rule-based model performed better than the LMNN and MLR models, in terms of the simulation performance criteria established, as the root mean square error, the Nash–Sutcliffe efficiency, the correlation coefficient and the Bias.

  16. Simulation Model of Magnetic Levitation Based on NARX Neural Networks

    Directory of Open Access Journals (Sweden)

    Dragan Antić

    2013-04-01

    Full Text Available In this paper, we present analysis of different training types for nonlinear autoregressive neural network, used for simulation of magnetic levitation system. First, the model of this highly nonlinear system is described and after that the Nonlinear Auto Regressive eXogenous (NARX of neural network model is given. Also, numerical optimization techniques for improved network training are described. It is verified that NARX neural network can be successfully used to simulate real magnetic levitation system if suitable training procedure is chosen, and the best two training types, obtained from experimental results, are described in details.

  17. Predicting chick body mass by artificial intelligence-based models

    Directory of Open Access Journals (Sweden)

    Patricia Ferreira Ponciano Ferraz

    2014-07-01

    Full Text Available The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks - with the variables dry-bulb air temperature, duration of thermal stress (days, chick age (days, and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs and neuro-fuzzy networks (NFNs. The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

  18. Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater

    Science.gov (United States)

    Manu, D. S.; Thalla, Arun Kumar

    2017-01-01

    The current work demonstrates the support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) modeling to assess the removal efficiency of Kjeldahl Nitrogen of a full-scale aerobic biological wastewater treatment plant. The influent variables such as pH, chemical oxygen demand, total solids (TS), free ammonia, ammonia nitrogen and Kjeldahl Nitrogen are used as input variables during modeling. Model development focused on postulating an adaptive, functional, real-time and alternative approach for modeling the removal efficiency of Kjeldahl Nitrogen. The input variables used for modeling were daily time series data recorded at wastewater treatment plant (WWTP) located in Mangalore during the period June 2014-September 2014. The performance of ANFIS model developed using Gbell and trapezoidal membership functions (MFs) and SVM are assessed using different statistical indices like root mean square error, correlation coefficients (CC) and Nash Sutcliff error (NSE). The errors related to the prediction of effluent Kjeldahl Nitrogen concentration by the SVM modeling appeared to be reasonable when compared to that of ANFIS models with Gbell and trapezoidal MF. From the performance evaluation of the developed SVM model, it is observed that the approach is capable to define the inter-relationship between various wastewater quality variables and thus SVM can be potentially applied for evaluating the efficiency of aerobic biological processes in WWTP.

  19. Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

    Institute of Scientific and Technical Information of China (English)

    Gurmanik KAUR‡; Ajat Shatru ARORA; Vijender Kumar JAIN

    2015-01-01

    Accurate blood pressure (BP) measurement is essential in epidemiological studies, screening programmes, and re-search studies as well as in clinical practice for the early detection and prevention of high BP-related risks such as coronary heart disease, stroke, and kidney failure. Posture of the participant plays a vital role in accurate measurement of BP. Guidelines on measurement of BP contain recommendations on the position of the back of the participants by advising that they should sit with supported back to avoid spuriously high readings. In this work, principal component analysis (PCA) is fused with forward stepwise regression (SWR), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and the least squares support vector machine (LS-SVM) model for the prediction of BP reactivity to an unsupported back in normotensive and hypertensive participants. PCA is used to remove multi-collinearity among anthropometric predictor variables and to select a subset of com-ponents, termed‘principal components’ (PCs), from the original dataset. The selected PCs are fed into the proposed models for modeling and testing. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that a PCA-based LS-SVM (PCA-LS-SVM) model is a promising approach for the prediction of BP reactivity in com-parison to others. This assessment demonstrates the importance and advantages posed by hybrid models for the prediction of variables in biomedical research studies.

  20. Impact of Hybrid Intelligent Computing in Identifying Constructive Weather Parameters for Modeling Effective Rainfall Prediction

    Directory of Open Access Journals (Sweden)

    M. Sudha

    2015-12-01

    Full Text Available Uncertain atmosphere is a prevalent factor affecting the existing prediction approaches. Rough set and fuzzy set theories as proposed by Pawlak and Zadeh have become an effective tool for handling vagueness and fuzziness in the real world scenarios. This research work describes the impact of Hybrid Intelligent System (HIS for strategic decision support in meteorology. In this research a novel exhaustive search based Rough set reduct Selection using Genetic Algorithm (RSGA is introduced to identify the significant input feature subset. The proposed model could identify the most effective weather parameters efficiently than other existing input techniques. In the model evaluation phase two adaptive techniques were constructed and investigated. The proposed Artificial Neural Network based on Back Propagation learning (ANN-BP and Adaptive Neuro Fuzzy Inference System (ANFIS was compared with existing Fuzzy Unordered Rule Induction Algorithm (FURIA, Structural Learning Algorithm on Vague Environment (SLAVE and Particle Swarm OPtimization (PSO. The proposed rainfall prediction models outperformed when trained with the input generated using RSGA. A meticulous comparison of the performance indicates ANN-BP model as a suitable HIS for effective rainfall prediction. The ANN-BP achieved 97.46% accuracy with a nominal misclassification rate of 0.0254 %.

  1. Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement.

    Science.gov (United States)

    Chiang, Shu-Yin; Kan, Yao-Chiang; Chen, Yun-Shan; Tu, Ying-Ching; Lin, Hsueh-Chun

    2016-12-03

    Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.

  2. Modeling Crossing Behavior of Drivers at Unsignalized Intersections with Consideration of Risk Perception

    Directory of Open Access Journals (Sweden)

    Liu Miaomiao

    2016-01-01

    Full Text Available Drivers’ risk perception is vital to driving behavior and traffic safety. In the dynamic interaction of a driver-vehicle-environment system, drivers’ risk perception changes dynamically. This study focused on drivers’ risk perception at unsignalized intersections in China and analyzed drivers’ crossing behavior. Based on cognitive psychology theory and an adaptive neuro-fuzzy inference system, quantitative models of drivers’ risk perception were established for the crossing processes between two straight-moving vehicles from the orthogonal direction. The acceptable risk perception levels of drivers were identified using a self-developed data analysis method. Based on game theory, the relationship among the quantitative value of drivers’ risk perception, acceptable risk perception level, and vehicle motion state was analyzed. The models of drivers’ crossing behavior were then established. Finally, the behavior models were validated using data collected from real-world vehicle movements and driver decisions. The results showed that the developed behavior models had both high accuracy and good applicability. This study would provide theoretical and algorithmic references for the microscopic simulation and active safety control system of vehicles.

  3. Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement

    Science.gov (United States)

    Chiang, Shu-Yin; Kan, Yao-Chiang; Chen, Yun-Shan; Tu, Ying-Ching; Lin, Hsueh-Chun

    2016-01-01

    Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC. PMID:27918482

  4. Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement

    Directory of Open Access Journals (Sweden)

    Shu-Yin Chiang

    2016-12-01

    Full Text Available Ubiquitous health care (UHC is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN. The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.

  5. Box –Jenkins Models For Forecasting The Daily Degrees Of Temperature In Sulaimani City.

    Directory of Open Access Journals (Sweden)

    Samira Muhammad Salh

    2014-02-01

    Full Text Available The Auto-regressive model in the time series is regarded one of the statistical articles which is more used because it gives us a simple method to limit the relation between variables time series. More over it is one of Box –Jenkins models to limit the time series in the forecasting the value of phenomenon in the future so that study aims for the practical analysis studying for the auto-regressive models in the time series, through one of Box –Jenkins models for forecasting the daily degrees of temperature in Sulaimani city for the year (2012- Sept.2013 and then for building a sample in the way of special data in the degrees of temperature and its using in the calculating the future forecasting . the style which is used is the descriptive and analyzing by the help of data that is dealt with statistically and which is collected from the official resources To reach his mentioned aim , the discussion of the following items has been done by the theoretical part which includes the idea of time series and its quality and the autocorrelation and Box –Jenkins and then the practical part which includes the statistical analysis for the data and the discussion of the theoretical part, so they reached to a lot of conclusions as it had come in the practical study for building autoregressive models of time series as the mode was very suitable is the auto-regressive model and model moving average by the degree (1,1,1.

  6. Modeling the Effects of Light and Sucrose on In Vitro Propagated Plants: A Multiscale System Analysis Using Artificial Intelligence Technology

    Science.gov (United States)

    Gago, Jorge; Martínez-Núñez, Lourdes; Landín, Mariana; Flexas, Jaume; Gallego, Pedro P.

    2014-01-01

    Background Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. Methodology and Principal Findings In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122–130 µmol m−2 s−1. Conclusions Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work. PMID:24465829

  7. Shear wave prediction using committee fuzzy model constrained by lithofacies, Zagros basin, SW Iran

    Science.gov (United States)

    Shiroodi, Sadjad Kazem; Ghafoori, Mohammad; Ansari, Hamid Reza; Lashkaripour, Golamreza; Ghanadian, Mostafa

    2017-02-01

    The main purpose of this study is to introduce the geological controlling factors in improving an intelligence-based model to estimate shear wave velocity from seismic attributes. The proposed method includes three main steps in the framework of geological events in a complex sedimentary succession located in the Persian Gulf. First, the best attributes were selected from extracted seismic data. Second, these attributes were transformed into shear wave velocity using fuzzy inference systems (FIS) such as Sugeno's fuzzy inference (SFIS), adaptive neuro-fuzzy inference (ANFIS) and optimized fuzzy inference (OFIS). Finally, a committee fuzzy machine (CFM) based on bat-inspired algorithm (BA) optimization was applied to combine previous predictions into an enhanced solution. In order to show the geological effect on improving the prediction, the main classes of predominate lithofacies in the reservoir of interest including shale, sand, and carbonate were selected and then the proposed algorithm was performed with and without lithofacies constraint. The results showed a good agreement between real and predicted shear wave velocity in the lithofacies-based model compared to the model without lithofacies especially in sand and carbonate.

  8. PENERAPAN METODE VECTOR AUTO REGRESSION DALAM INTERAKSI KEBIJAKAN FISKAL DAN MONETER DI INDONESIA

    Directory of Open Access Journals (Sweden)

    Adrian Sutawijaya

    2013-06-01

    Full Text Available The purpose of this study is to analyze the interaction of fiscal and monetary policy in Indonesia, especially after the introduction of fiscal and monetary policy shocks. The research method used is the vector autoregression (VAR. VAR is usually used for projecting coherent system variables and time to analyze the dynamic impact of disturbance factors contained in the system variables. Variables used in this study is the level of interest rates as a proxy for monetary policy instruments, government expenditures as a proxy for fiscal policy, inflation rates and national income. The results show that fiscal policy is a negative shock to inflation and responded with a tight monetary policy, while the shock in monetary policy will reduce national income. The application of fiscal and monetary policies that will effectively promote economic growth.

  9. Evaluating the Performance of Wavelet-based Data-driven Models for Multistep-ahead Flood Forecasting in an Urbanized Watershed

    Science.gov (United States)

    Kasaee Roodsari, B.; Chandler, D. G.

    2015-12-01

    A real-time flood forecast system is presented to provide emergency management authorities sufficient lead time to execute plans for evacuation and asset protection in urban watersheds. This study investigates the performance of two hybrid models for real-time flood forecasting at different subcatchments of Ley Creek watershed, a heavily urbanized watershed in the vicinity of Syracuse, New York. Hybrid models include Wavelet-Based Artificial Neural Network (WANN) and Wavelet-Based Adaptive Neuro-Fuzzy Inference System (WANFIS). Both models are developed on the basis of real time stream network sensing. The wavelet approach is applied to decompose the collected water depth timeseries to Approximation and Detail components. The Approximation component is then used as an input to ANN and ANFIS models to forecast water level at lead times of 1 to 10 hours. The performance of WANN and WANFIS models are compared to ANN and ANFIS models for different lead times. Initial results demonstrated greater predictive power of hybrid models.

  10. Identification of a Typical CSTR Using Optimal Focused Time Lagged Recurrent Neural Network Model with Gamma Memory Filter

    Directory of Open Access Journals (Sweden)

    S. N. Naikwad

    2009-01-01

    Full Text Available A focused time lagged recurrent neural network (FTLR NN with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes temporal relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. The standard back propagation algorithm with momentum term has been proposed in this model. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE, and correlation coefficient on testing data set. Finally effects of different norms are tested along with variation in gamma memory filter. It is demonstrated that dynamic NN model has a remarkable system identification capability for the problems considered in this paper. Thus FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is a major contribution of this paper.

  11. Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time

    Science.gov (United States)

    Prasad, Kanchan; Gorai, Amit Kumar; Goyal, Pramila

    2016-03-01

    This study aims to develop adaptive neuro-fuzzy inference system (ANFIS) for forecasting of daily air pollution concentrations of five air pollutants [sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3) and particular matters (PM10)] in the atmosphere of a Megacity (Howrah). Air pollution in the city (Howrah) is rising in parallel with the economics and thus observing, forecasting and controlling the air pollution becomes increasingly important due to the health impact. ANFIS serve as a basis for constructing a set of fuzzy IF-THEN rules, with appropriate membership functions to generate the stipulated input-output pairs. The ANFIS model predictor considers the value of meteorological factors (pressure, temperature, relative humidity, dew point, visibility, wind speed, and precipitation) and previous day's pollutant concentration in different combinations as the inputs to predict the 1-day advance and same day air pollution concentration. The concentration value of five air pollutants and seven meteorological parameters of the Howrah city during the period 2009 to 2011 were used for development of the ANFIS model. Collinearity tests were conducted to eliminate the redundant input variables. A forward selection (FS) method is used for selecting the different subsets of input variables. Application of collinearity tests and FS techniques reduces the numbers of input variables and subsets which helps in reducing the computational cost and time. The performances of the models were evaluated on the basis of four statistical indices (coefficient of determination, normalized mean square error, index of agreement, and fractional bias).

  12. A nonlinear spatio-temporal lumping of radar rainfall for modeling multi-step-ahead inflow forecasts by data-driven techniques

    Science.gov (United States)

    Chang, Fi-John; Tsai, Meng-Jung

    2016-04-01

    Accurate multi-step-ahead inflow forecasting during typhoon periods is extremely crucial for real-time reservoir flood control. We propose a spatio-temporal lumping of radar rainfall for modeling inflow forecasts to mitigate time-lag problems and improve forecasting accuracy. Spatial aggregation of radar cells is made based on the sub-catchment partitioning obtained from the Self-Organizing Map (SOM), and then flood forecasting is made by the Adaptive Neuro Fuzzy Inference System (ANFIS) models coupled with a 2-staged Gamma Test (2-GT) procedure that identifies the optimal non-trivial rainfall inputs. The Shihmen Reservoir in northern Taiwan is used as a case study. The results show that the proposed methods can, in general, precisely make 1- to 4-hour-ahead forecasts and the lag time between predicted and observed flood peaks could be mitigated. The constructed ANFIS models with only two fuzzy if-then rules can effectively categorize inputs into two levels (i.e. high and low) and provide an insightful view (perspective) of the rainfall-runoff process, which demonstrate their capability in modeling the complex rainfall-runoff process. In addition, the confidence level of forecasts with acceptable error can reach as high as 97% at horizon t+1 and 77% at horizon t+4, respectively, which evidently promotes model reliability and leads to better decisions on real-time reservoir operation during typhoon events.

  13. Performance evaluation of agricultural drainage water using modeling and statistical approaches

    Directory of Open Access Journals (Sweden)

    Mahmoud Nasr

    2016-06-01

    Full Text Available This study assessed spatial variations in physical and chemical properties of an agricultural drain near Borg El-Arab city, Alexandria, Egypt. Pearson’s correlation coefficient indicated that salinity had strong correlations with total dissolved solids (TDS (r 0.999, p < 0.001 and Cl− (r 0.807, p 0.016, whereas, pH was considerably affected by temperature (r 0.674, p 0.067, oxidation reduction potential (ORP (r 0.866, p 0.006 and NO3− (r 0.731, p 0.039. Those results were further confirmed by applying an adaptive neuro-fuzzy inference system and regression models. Moreover, principal component analysis (PCA indicated that PC1 explained 41.1% of the total variance, and had high loadings of TDS (0.46, salinity (0.46 and Cl− (0.48. Additionally, PC2 accounted for 35.2% of the total variance, and had high loadings of pH (0.53, temperature (0.48, ORP (0.40 and NO3− (0.47. The present study revealed that artificial intelligence and PCA could be used to effectively reduce the number of physicochemical parameters that may assist in the description of drainage water quality. It is recommended that the current status of the drain is suitable for reuse in irrigation purposes except at few locations containing high salinity.

  14. A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis.

    Science.gov (United States)

    Akutekwe, Arinze; Seker, Huseyin

    2014-01-01

    Computational and machine learning techniques have been applied in identifying biomarkers and constructing predictive models for diagnosis of hypertension. Strategies such as improved classification rules based on decision trees have been proposed. Other techniques such as Fuzzy Expert Systems (FES) and Neuro-Fuzzy Systems (NFS) have recently been applied. However, these methods lack the ability to detect temporal relationships among biomarker genes that will aid better understanding of the mechanism of hypertension disease. In this paper we apply a proposed two-stage bio-network construction approach that combines the power and computational efficiency of classification methods with the well-established predictive ability of Dynamic Bayesian Network. We demonstrate our method using the analysis of male young-onset hypertension microarray dataset. Four key genes were identified by the Least Angle Shrinkage and Selection Operator (LASSO) and three Support Vector Machine Recursive Feature Elimination (SVM-RFE) methods. Results show that cell regulation FOXQ1 may inhibit the expression of focusyltransferase-6 (FUT6) and that ABCG1 ATP-binding cassette sub-family G may also play inhibitory role against NR2E3 nuclear receptor sub-family 2 and CGB2 Chromatin Gonadotrophin.

  15. Application of system identification modelling to solar hybrid systems for predicting radiation, temperature and load

    Energy Technology Data Exchange (ETDEWEB)

    Sinha, S.; Matsumoto, Tsuyoshi; Kojima, Toshinori [Seikei University, Tokyo (Japan). Dept. of Industrial Chemistry; Sanjay Kumar [Kyoto University (Japan). Dept. of Global Environment Engineering

    2001-03-01

    Uncertainties in local solar radiation, ambient temperature and thermal load data have been one of the major factors limiting the reliability and efficiency of solar thermal hybrid systems. In the present paper, moving average auto regressive erogenous (ARX) model based reasoning has been mooted and modified to include moving average method, as an effective tool for predictions of these data. The results show that the method is quite robust and is capable of predicting fairly accurate results, which would make these systems more viable in areas where meteorological data are not available or vague. (author)

  16. NEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS

    Directory of Open Access Journals (Sweden)

    A.N. Moroz

    2016-03-01

    Full Text Available Purpose. Form a neuro-fuzzy network based on temperature monitoring of overhead transmission line for the prediction modes of the electrical network. Methodology. To predict the load capacity of the overhead line architecture provides the use of neuro-fuzzy network based on temperature monitoring of overhead line. The proposed neuro-fuzzy network has a four-layer architecture with direct transmission of information. To create a full mesh network architecture based on hybrid neural elements with power estimation accuracy of the following two stages of the procedure: - in the first stage a core network (without power estimation accuracy is generated; - in the second stage architecture and network parameters are fixed obtained during the first stage, and it is added to the block estimation accuracy, the input signals which are all input, internal and output signals of the core network, as well as additional input signals. Results. Formed neuro-fuzzy network based on temperature monitoring of overhead line. Originality. A distinctive feature of the proposed network is the ability to process information specified in the different scales of measurement, and high performance for prediction modes mains. Practical value. The monitoring system will become a tool parameter is measuring the temperature of the wire, which will, based on a retrospective analysis of the accumulated information on the parameters to predict the thermal resistance of the HV line and as a result carry out the calculation of load capacity in real time.

  17. General expression for linear and nonlinear time series models

    Institute of Scientific and Technical Information of China (English)

    Ren HUANG; Feiyun XU; Ruwen CHEN

    2009-01-01

    The typical time series models such as ARMA, AR, and MA are founded on the normality and stationarity of a system and expressed by a linear difference equation; therefore, they are strictly limited to the linear system. However, some nonlinear factors are within the practical system; thus, it is difficult to fit the model for real systems with the above models. This paper proposes a general expression for linear and nonlinear auto-regressive time series models (GNAR). With the gradient optimization method and modified AIC information criteria integrated with the prediction error, the parameter estimation and order determination are achieved. The model simulation and experiments show that the GNAR model can accurately approximate to the dynamic characteristics of the most nonlinear models applied in academics and engineering. The modeling and prediction accuracy of the GNAR model is superior to the classical time series models. The proposed GNAR model is flexible and effective.

  18. Video Coding and Modeling with Applications to ATM Multiplexing

    Science.gov (United States)

    Nguyen, Hien

    A new vector quantization (VQ) coding method based on optimized concentric shell partitioning of the image space is proposed. The advantages of using the concentric shell partition vector quantizer (CSPVQ) are that it is very fast and the image patterns found in each different subspace can be more effectively coded by using a codebook that is best matched to that particular subspace. For intra-frame coding, the CSPVQ is shown to have the same performance, if not better, than the optimized gain-shape VQ in terms of encoded picture quality while it definitely surpasses the gain-shape VQ in term of computational complexity. A variable bit rate (VBR) video coder for moving video is then proposed where the idea of CSPVQ is coupled with the idea of regular quadtree decomposition to further reduce the bit rate of the encoded picture sequence. The usefulness of a quadtree coding technique comes from the fact that different homogeneous regions occurring within an image can be compactly represented by various nodes in a quadtree. It is found that this image representation technique is particularly useful in providing a low bit rate video encoder without compromising the image quality when it is used in conjunction with the CSPVQ. The characteristics of the VBR coder's output as applied to ATM transmission are investigated. Three video models are used to study the performance of the ATM multiplexer. These models are the auto regressive (AR) model, the auto regressive hidden Markov model (AR-HMM), and the fluid flow uniform arrival and service (UAS) model. The AR model is allowed to have arbitrary order and is used to model a video source which has a constant amount of motion, that is, a stationary video source. The AR-HMM is a more general video model which is based on the idea of auto regressive hidden Markov chain formulated by Baum and is used to describe highly non-stationary sources. Hence, it is expected that the AR-HMM model may also be used top represent a video

  19. Evaluation of statistical models for forecast errors from the HBV model

    Science.gov (United States)

    Engeland, Kolbjørn; Renard, Benjamin; Steinsland, Ingelin; Kolberg, Sjur

    2010-04-01

    SummaryThree statistical models for the forecast errors for inflow into the Langvatn reservoir in Northern Norway have been constructed and tested according to the agreement between (i) the forecast distribution and the observations and (ii) median values of the forecast distribution and the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order auto-regressive model was constructed for the forecast errors. The parameters were conditioned on weather classes. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order auto-regressive model was constructed for the forecast errors. For the third model positive and negative errors were modeled separately. The errors were first NQT-transformed before conditioning the mean error values on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: we wanted (a) the forecast distribution to be reliable; (b) the forecast intervals to be narrow; (c) the median values of the forecast distribution to be close to the observed values. Models 1 and 2 gave almost identical results. The median values improved the forecast with Nash-Sutcliffe R eff increasing from 0.77 for the original forecast to 0.87 for the corrected forecasts. Models 1 and 2 over-estimated the forecast intervals but gave the narrowest intervals. Their main drawback was that the distributions are less reliable than Model 3. For Model 3 the median values did not fit well since the auto-correlation was not accounted for. Since Model 3 did not benefit from the potential variance reduction that lies in bias estimation and removal it gave on average wider forecasts intervals than the two other models. At the same time Model 3 on average slightly under-estimated the forecast intervals, probably explained by the use of average measures to evaluate the fit.

  20. Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS

    Science.gov (United States)

    Tien Bui, Dieu; Pradhan, Biswajeet; Nampak, Haleh; Bui, Quang-Thanh; Tran, Quynh-An; Nguyen, Quoc-Phi

    2016-09-01

    This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE = 0.306, MAE = 0.094, AUC = 0.962) and validation dataset (RMSE = 0.362, MAE = 0.130, AUC = 0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas.

  1. Pan evaporation modeling using six different heuristic computing methods in different climates of China

    Science.gov (United States)

    Wang, Lunche; Kisi, Ozgur; Zounemat-Kermani, Mohammad; Li, Hui

    2017-01-01

    Pan evaporation (Ep) plays important roles in agricultural water resources management. One of the basic challenges is modeling Ep using limited climatic parameters because there are a number of factors affecting the evaporation rate. This study investigated the abilities of six different soft computing methods, multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at various sites crossing a wide range of climates during 1961-2000 are used for model development and validation. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations using local input combinations (for example, the MAE (mean absolute errors), RMSE (root mean square errors), and determination coefficient (R2) are 0.314 mm/day, 0.405 mm/day and 0.988, respectively for HEB station), while GRNN model performed better in Tibetan Plateau (MAE, RMSE and R2 are 0.459 mm/day, 0.592 mm/day and 0.932, respectively). The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ (Beijing), CQ (Chongqing) and HK (Haikou) stations. Therefore, it can be concluded that Ep could be successfully predicted using above models in hydrological modeling studies.

  2. Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method

    Science.gov (United States)

    Nourani, Vahid; Mousavi, Shahram

    2016-05-01

    Uncertainties of the field parameters, noise of the observed data and unknown boundary conditions are the main factors involved in the groundwater level (GL) time series which limit the modeling and simulation of GL. This paper presents a hybrid artificial intelligence-meshless model for spatiotemporal GL modeling. In this way firstly time series of GL observed in different piezometers were de-noised using threshold-based wavelet method and the impact of de-noised and noisy data was compared in temporal GL modeling by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). In the second step, both ANN and ANFIS models were calibrated and verified using GL data of each piezometer, rainfall and runoff considering various input scenarios to predict the GL at one month ahead. In the final step, the simulated GLs in the second step of modeling were considered as interior conditions for the multiquadric radial basis function (RBF) based solve of governing partial differential equation of groundwater flow to estimate GL at any desired point within the plain where there is not any observation. In order to evaluate and compare the GL pattern at different time scales, the cross-wavelet coherence was also applied to GL time series of piezometers. The results showed that the threshold-based wavelet de-noising approach can enhance the performance of the modeling up to 13.4%. Also it was found that the accuracy of ANFIS-RBF model is more reliable than ANN-RBF model in both calibration and validation steps.

  3. Neuro-fuzzy-wavelet network for detection and classification of the voltage disturbances in electrical power system; Rede neuro-fuzzy-wavelet para deteccao e classificacao de anomalias de tensao em sistemas eletricos de potencia

    Energy Technology Data Exchange (ETDEWEB)

    Malange, Fernando C.V. [Universidade do Estado de Mato Grosso (UEMT), Caceres, MT (Brazil). Dept. de Computacao], E-mail: fmalange@gmail.com; Minussi, Carlos R. [Universidade Estadual Paulista (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], E-mail: minussi@dee.feis.unesp.br

    2009-07-01

    A methodology for identifying and classifying voltage disturbances (harmonics, voltage sag, etc.) using fuzzy ARTMAP neural networks is presented. It is an ART (adaptive resonance theory) architecture family neural network that presents the stability and plasticity properties, which are fundamental requests for developing a reliable electrical systems with reduced processing time. Stability means a guarantee of good solutions; plasticity allows realize the training without restart the system every time there are new patterns to be stored in a weight matrix of the neural network. The training is realized from the wave forms provided by the acquisition data system, using the wavelets theory to generate the coefficients that constitute the input patterns of the neural network. Results from simulations show that the accuracy index is nearly 100%. (author)

  4. Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification.

    Science.gov (United States)

    Asadi, Somayeh; Hassan, Marwa; Nadiri, Ataallah; Dylla, Heather

    2014-01-01

    In recent years, the application of titanium dioxide (TiO₂) as a photocatalyst in asphalt pavement has received considerable attention for purifying ambient air from traffic-emitted pollutants via photocatalytic processes. In order to control the increasing deterioration of ambient air quality, urgent and proper risk assessment tools are deemed necessary. However, in practice, monitoring all process parameters for various operating conditions is difficult due to the complex and non-linear nature of air pollution-based problems. Therefore, the development of models to predict air pollutant concentrations is very useful because it can provide early warnings to the population and also reduce the number of measuring sites. This study used artificial neural network (ANN) and neuro-fuzzy (NF) models to predict NOx concentration in the air as a function of traffic count (Tr) and climatic conditions including humidity (H), temperature (T), solar radiation (S), and wind speed (W) before and after the application of TiO₂ on the pavement surface. These models are useful for modeling because of their ability to be trained using historical data and because of their capability for modeling highly non-linear relationships. To build these models, data were collected from a field study where an aqueous nano TiO₂ solution was sprayed on a 0.2-mile of asphalt pavement in Baton Rouge, LA. Results of this study showed that the NF model provided a better fitting to NOx measurements than the ANN model in the training, validation, and test steps. Results of a parametric study indicated that traffic level, relative humidity, and solar radiation had the most influence on photocatalytic efficiency.

  5. An Adaptive Network-based Fuzzy Inference System for the detection of thermal and TEC anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake of 11 August 2012

    Science.gov (United States)

    Akhoondzadeh, M.

    2013-09-01

    Anomaly detection is extremely important for forecasting the date, location and magnitude of an impending earthquake. In this paper, an Adaptive Network-based Fuzzy Inference System (ANFIS) has been proposed to detect the thermal and Total Electron Content (TEC) anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake jolted in 11 August 2012 NW Iran. ANFIS is the famous hybrid neuro-fuzzy network for modeling the non-linear complex systems. In this study, also the detected thermal and TEC anomalies using the proposed method are compared to the results dealing with the observed anomalies by applying the classical and intelligent methods including Interquartile, Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. The duration of the dataset which is comprised from Aqua-MODIS Land Surface Temperature (LST) night-time snapshot images and also Global Ionospheric Maps (GIM), is 62 days. It can be shown that, if the difference between the predicted value using the ANFIS method and the observed value, exceeds the pre-defined threshold value, then the observed precursor value in the absence of non seismic effective parameters could be regarded as precursory anomaly. For two precursors of LST and TEC, the ANFIS method shows very good agreement with the other implemented classical and intelligent methods and this indicates that ANFIS is capable of detecting earthquake anomalies. The applied methods detected anomalous occurrences 1 and 2 days before the earthquake. This paper indicates that the detection of the thermal and TEC anomalies derive their credibility from the overall efficiencies and potentialities of the five integrated methods.

  6. An Adaptive Neuro-Fuzzy Inference Distributed Power Flow Controller (DPFC In Multi-Machine Power Systems

    Directory of Open Access Journals (Sweden)

    Gurrala Madhusudhan Rao

    2014-10-01

    Full Text Available Abstract: The main theme of the paper which deals with the enhancing steady-state and dynamics performance of the power grids by Flexible AC Transmission System (FACTS based on computational intelligence. The proposed technique will be applied to solve real problems in a power grid. The FACTS device, which will be used in the paper, is the most promising one, which known as the Distributed Power Flow Controller (DPFC. The paper achieves the optimization of the type, the location and the size of the power and control elements for DPFC to optimize the system performance. The paper derives the criteria to install the DPFC in an optimal location with optimal parameters and then designs an AI based damping controller for enhancing power system dynamic performance. In this paper, for every operating point genetic algorithm is used to search for controllers’ parameters, parameters found at certain operating point are different from those found at others. ANFISs are required in this case to recognize the appropriate parameters for each operating point.

  7. Restoration of Normal Frequency Affected by Small Load Variations Through HVDC link Using Neuro-Fuzzy Approach

    Directory of Open Access Journals (Sweden)

    Anil Kumar Sharma,

    2011-03-01

    Full Text Available HVDC power transmission is coming up with merits to replace the EHV-AC system. The controller inverter operation can successfully regulate the power in HVDC link leading to fulfillment ofpower demand in A.C. networks caused by sudden rise in loading. Since overloading and unloading both lead to the departure of operating frequency below or above normal, its control for normalization isexercised through control of power flow in HVDC Link. Also the same is achieved by adjusting firing delay angle intelligently. This paper aims at providing automation to frequency normalization after it hasundergone changes from normal value of 50 Hz, due to sudden and sustained increase in load or due to working of generations at increased loads in H.V.D.C transmission systems. It is planned to automate the restoration of departed frequency to normal value by arranging the change in firing delay angle i.e. α of the converter of H.V.D.C. link till the increased load demand is met with. An adaptive control system has been devised which controls the firing delay angle on the basis of decisions given by a fuzzy controller. Also the fuzzy controls are decided by the departure in frequency from normal. The results have been found to be encouraging.

  8. Restoration of Normal Frequency Affected by Small Load Variations Through HVDC link Using Neuro-Fuzzy Approach

    OpenAIRE

    Anil Kumar Sharma,; Dr. G. K. Joshi,; Tarun Kumar De; Shyam Krishan Joshi

    2011-01-01

    HVDC power transmission is coming up with merits to replace the EHV-AC system. The controller inverter operation can successfully regulate the power in HVDC link leading to fulfillment ofpower demand in A.C. networks caused by sudden rise in loading. Since overloading and unloading both lead to the departure of operating frequency below or above normal, its control for normalization isexercised through control of power flow in HVDC Link. Also the same is achieved by adjusting firing delay ang...

  9. Improving motor imagery classification with a new BCI design using neuro-fuzzy S-dFasArt.

    Science.gov (United States)

    Cano-Izquierdo, Jose-Manuel; Ibarrola, Julio; Almonacid, Miguel

    2012-01-01

    This paper presents an algorithm based on neural networks and fuzzy theory (S-dFasArt) to classify spontaneous mental activities from electroencephalogram (EEG) signals, in order to operate a noninvasive brain-computer interface. The focus is placed on the three-class problem, left-hand movement imagination, right movement imagination and word generation. The algorithm allows a supervised classification of temporal patterns improving the classification rates of the BCI Competition III (Data Set V: multiclass problem, continuous EEG). Using the precomputed data supplied for the competition and following the rules established there, a new method based on S-dFasArt, along with rule prune and voting strategy is proposed. The results have been compared with other published methods improving their success rates.

  10. EMG signals characterization in three states of contraction by fuzzy network and feature extraction

    CERN Document Server

    Mokhlesabadifarahani, Bita

    2015-01-01

    Neuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motion abilities of an individual. This brief highlights a systematic method for detection of the level of muscle power declining in musculoskeletal and Neuro-muscular disorders. The neuro-fuzzy system is trained with 70 percent of the recorded Electromyography (EMG) cut off window and then used for classification and modeling purposes. The neuro-fuzzy classifier is validated in comparison to some other well-known classifiers in classification of the recorded EMG signals with the three states of contractions corresponding to the extracted features. Different structures of the neuro-fuzzy classifier are also comparatively analyzed to find the optimum structure of the classifier used.

  11. Modelling and analysis of ozone concentration by artificial intelligent techniques for estimating air quality

    Science.gov (United States)

    Taylan, Osman

    2017-02-01

    High ozone concentration is an important cause of air pollution mainly due to its role in the greenhouse gas emission. Ozone is produced by photochemical processes which contain nitrogen oxides and volatile organic compounds in the lower atmospheric level. Therefore, monitoring and controlling the quality of air in the urban environment is very important due to the public health care. However, air quality prediction is a highly complex and non-linear process; usually several attributes have to be considered. Artificial intelligent (AI) techniques can be employed to monitor and evaluate the ozone concentration level. The aim of this study is to develop an Adaptive Neuro-Fuzzy inference approach (ANFIS) to determine the influence of peripheral factors on air quality and pollution which is an arising problem due to ozone level in Jeddah city. The concentration of ozone level was considered as a factor to predict the Air Quality (AQ) under the atmospheric conditions. Using Air Quality Standards of Saudi Arabia, ozone concentration level was modelled by employing certain factors such as; nitrogen oxide (NOx), atmospheric pressure, temperature, and relative humidity. Hence, an ANFIS model was developed to observe the ozone concentration level and the model performance was assessed by testing data obtained from the monitoring stations established by the General Authority of Meteorology and Environment Protection of Kingdom of Saudi Arabia. The outcomes of ANFIS model were re-assessed by fuzzy quality charts using quality specification and control limits based on US-EPA air quality standards. The results of present study show that the ANFIS model is a comprehensive approach for the estimation and assessment of ozone level and is a reliable approach to produce more genuine outcomes.

  12. Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM2.5

    Science.gov (United States)

    Ausati, Shadi; Amanollahi, Jamil

    2016-10-01

    Since Sanandaj is considered one of polluted cities of Iran, prediction of any type of pollution especially prediction of suspended particles of PM2.5, which are the cause of many diseases, could contribute to health of society by timely announcements and prior to increase of PM2.5. In order to predict PM2.5 concentration in the Sanandaj air the hybrid models consisting of an ensemble empirical mode decomposition and general regression neural network (EEMD-GRNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), principal component regression (PCR), and linear model such as multiple liner regression (MLR) model were used. In these models the data of suspended particles of PM2.5 were the dependent variable and the data related to air quality including PM2.5, PM10, SO2, NO2, CO, O3 and meteorological data including average minimum temperature (Min T), average maximum temperature (Max T), average atmospheric pressure (AP), daily total precipitation (TP), daily relative humidity level of the air (RH) and daily wind speed (WS) for the year 2014 in Sanandaj were the independent variables. Among the used models, EEMD-GRNN model with values of R2 = 0.90, root mean square error (RMSE) = 4.9218 and mean absolute error (MAE) = 3.4644 in the training phase and with values of R2 = 0.79, RMSE = 5.0324 and MAE = 3.2565 in the testing phase, exhibited the best function in predicting this phenomenon. It can be concluded that hybrid models have accurate results to predict PM2.5 concentration compared with linear model.

  13. A model-based approach to human identification using ECG

    Science.gov (United States)

    Homer, Mark; Irvine, John M.; Wendelken, Suzanne

    2009-05-01

    Biometrics, such as fingerprint, iris scan, and face recognition, offer methods for identifying individuals based on a unique physiological measurement. Recent studies indicate that a person's electrocardiogram (ECG) may also provide a unique biometric signature. Current techniques for identification using ECG rely on empirical methods for extracting features from the ECG signal. This paper presents an alternative approach based on a time-domain model of the ECG trace. Because Auto-Regressive Integrated Moving Average (ARIMA) models form a rich class of descriptors for representing the structure of periodic time series data, they are well-suited to characterizing the ECG signal. We present a method for modeling the ECG, extracting features from the model representation, and identifying individuals using these features.

  14. Using an Adaptative Fuzzy-Logic System to Optimize the Performances and the Reduction of Chattering Phenomenon in the Control of Induction Motor

    Directory of Open Access Journals (Sweden)

    M. M. Krishan

    2010-01-01

    Full Text Available Problem statement: Neural networks and fuzzy inference systems are becoming well-recognized tools of designing an identifier/controller capable of perceiving the operating environment and imitating a human operator with high performance. Also, by combining these two features, more versatile and robust models, called neuro-fuzzy architectures have been developed. The mo Approach: Motivation behind the use of neuro-fuzzy approaches was based on the complexity of real life systems, ambiguities on sensory information or time-varying nature of the system under investigation. In this way, the present contribution concerns the application of neuro-fuzzy approach in order to perform the responses of the speed regulation, ensure more robustness of the overall system and to reduce the chattering phenomenon introduced by sliding mode control which is very harmful to the actuators in our case and may excite the unmodeled dynamics of the system. Results: In fact, the aim of such a research consists first in simplifying the control of the motor by decoupling between two principles variables which provoque the torque in the motor by using the feedback linearization method. Then, using sliding mode controllers to give our process more robustness towards the variation of different parameters of the motor. However, the latter technique of control called sliding mode control caused an indesirable phenomenon which harmful and could leads to the deterioration of the inverters components called chattering. So, here the authors propose to use neuro-fuzzy systems to reduce this phenomenon and perform the performances of the adopted control process. The type of the neuro-fuzzy system used here is called: Adaptive Neuro Fuzzy Inference System (ANFIS. This neuro-fuzzy is destined to replace the speed fuzzy sliding mode controller after its training process. Conclusion: Therefore, from a control design consideration, the adopted neuro-fuzzy system has opened up a new

  15. CONTROL OF NONLINEAR PROCESS USING NEURAL NETWORK BASED MODEL PREDICTIVE CONTROL

    Directory of Open Access Journals (Sweden)

    Dr.A.TRIVEDI

    2011-04-01

    Full Text Available This paper presents a Neural Network based Model Predictive Control (NNMPC strategy to control nonlinear process. Multilayer Perceptron Neural Network (MLP is chosen to represent a Nonlinear Auto Regressive with eXogenous signal (NARX model of a nonlinear system. NARX dynamic model is based on feed-forward architecture and offers good approximation capabilities along with robustness and accuracy. Based on the identified neural model, a generalized predictive control (GPC algorithm is implemented to control the composition in acontinuous stirred tank reactor (CSTR, whose parameters are optimally determined by solving quadratic performance index using well known Levenberg-Marquardt and Quasi-Newton algorithm. NNMPC is tuned by selecting few horizon parameters and weighting factor. The tracking performance of the NNMPC is tested using different amplitude function as a reference signal on CSTR application. Also the robustness and performance is tested in the presence of disturbance on random reference signal.

  16. Modelling thermal comfort of visitors at urban squares in hot and arid climate using NN-ARX soft computing method

    Science.gov (United States)

    Kariminia, Shahab; Motamedi, Shervin; Shamshirband, Shahaboddin; Piri, Jamshid; Mohammadi, Kasra; Hashim, Roslan; Roy, Chandrabhushan; Petković, Dalibor; Bonakdari, Hossein

    2016-05-01

    Visitors utilize the urban space based on their thermal perception and thermal environment. The thermal adaptation engages the user's behavioural, physiological and psychological aspects. These aspects play critical roles in user's ability to assess the thermal environments. Previous studies have rarely addressed the effects of identified factors such as gender, age and locality on outdoor thermal comfort, particularly in hot, dry climate. This study investigated the thermal comfort of visitors at two city squares in Iran based on their demographics as well as the role of thermal environment. Assessing the thermal comfort required taking physical measurement and questionnaire survey. In this study, a non-linear model known as the neural network autoregressive with exogenous input (NN-ARX) was employed. Five indices of physiological equivalent temperature (PET), predicted mean vote (PMV), standard effective temperature (SET), thermal sensation votes (TSVs) and mean radiant temperature ( T mrt) were trained and tested using the NN-ARX. Then, the results were compared to the artificial neural network (ANN) and the adaptive neuro-fuzzy inference system (ANFIS). The findings showed the superiority of the NN-ARX over the ANN and the ANFIS. For the NN-ARX model, the statistical indicators of the root mean square error (RMSE) and the mean absolute error (MAE) were 0.53 and 0.36 for the PET, 1.28 and 0.71 for the PMV, 2.59 and 1.99 for the SET, 0.29 and 0.08 for the TSV and finally 0.19 and 0.04 for the T mrt.

  17. Optimal parametric modelling of measured short waves

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.

    or auto-regressive moving average algorithms which are the polynomical functions. Further reduced order autoregressive moving average spectral estimations are successfully applied to time series waves with different sampling rates. This paper emphasises...

  18. Linear prediction of atmospheric wave-fronts for tomographic Adaptive Optics systems: modelling and robustness assessment

    CERN Document Server

    Jackson, Kate; Lardiere, Olivier; Andersen, Dave; Bradley, Colin

    2015-01-01

    We use a theoretical frame-work to analytically assess temporal prediction error functions on von-Karman turbulence when a zonal representation of wave-fronts is assumed. Linear prediction models analysed include auto-regressive of order up to three, bilinear interpolation functions and a minimum mean square error predictor. This is an extension of the authors' previously published work (see ref. 2) in which the efficacy of various temporal prediction models was established. Here we examine the tolerance of these algorithms to specific forms of model errors, thus defining the expected change in behaviour of the previous results under less ideal conditions. Results show that +/- 100pc wind-speed error and +/- 50 deg are tolerable before the best linear predictor delivers poorer performance than the no-prediction case.

  19. Disaggregation modelling of spring discharges

    Directory of Open Access Journals (Sweden)

    Kirilova Bojilova Elena

    2004-12-01

    Full Text Available Disaggregation models are basically divided into three main groups: temporal, spatial and temporal-spatial. The focus of this paper is the application of temporal disaggregation models to disaggregate the seasonal flow in some large time intervals to sub-seasonal flows in some shorter time intervals. Two basic models are applied: the original model of Mejia and Rousselle and the corrected extended Lin model one-stage disaggregation. The flow totals from some karstic springs are used. Data for five springs in different areas of Bulgaria for the aims of the study are executed. The synthetic data generation for the chosen spring stations for a new realisation of thirty years is obtained. The multi-variate lag-one auto regressive model (AR(1 model is applied for generation of the annual flow sequences. The Lin model single- site is performed for thirty years generation period. The Lin model is an improvement compared to the original extended model. The new Lin approach succeeds in the preservation of the additivity as well as the moments. Applying the Lin model one-stage disaggregation results in consistent model parameter estimates. As a second step in the research multi-site disaggregation schemes are also applied.

  20. Nero-fuzzy modeling of the convection heat transfer coefficient for the nanofluid

    Science.gov (United States)

    Salehi, H.; Zeinali-Heris, S.; Esfandyari, M.; Koolivand, M.

    2013-04-01

    In this study, experiments were performed by six different volume fractions of Al2O3 nanoparticles in distilled water. Then, actual nanofluid Nusslet number compared by Adaptive neuro fuzzy inference system (ANFIS) predicted number in square cross-section duct in laminar flow under uniform heat flux condition. Statistical values, which quantify the degree of agreement between experimental observations and numerically calculated values, were found greater than 0.99 for all cases.

  1. A Brief Analysis Of The Tax Smoothing Hypothesis In Turkey

    Directory of Open Access Journals (Sweden)

    Mesut KARAKAS

    2014-06-01

    Full Text Available This study examines the existence of tax smoothing in the case of Turkey using data for the time period between 1923 and 2011. Unit root tests, auto-regression and vector auto-regression (VAR models are applied to tax rates, government expenditures and real output data. Unit root tests and auto-regression results initially point out the existence of tax smoothing in Turkey. However, further in-depth analyses by means of the vector auto-regression model provide strong evidence against the tax smoothing hypothesis for the Turkish case as contemporary tax rates can be predicted with using lagged values of tax rates and government spending rates.

  2. Long-Term Sunspot Number Prediction based on EMD Analysis and AR Model

    Institute of Scientific and Technical Information of China (English)

    Tong Xu; Jian Wu; Zhen-Sen Wu; Qiang Li

    2008-01-01

    The Empirical Mode Decomposition (EMD) and Auto-Regressive model (AR) are applied to a long-term prediction of sunspot numbers. With the sample data of sunspot numbers from 1848 to 1992, the method is evaluated by examining the measured data of the solar cycle 23 with the prediction: different time scale components are obtained by the EMD method and multi-step predicted values are combined to reconstruct the sunspot number time series. The result is remarkably good in comparison to the predictions made by the solar dynamo and precursor approaches for cycle 23. Sunspot numbers of the coming solar cycle 24 are obtained with the data from 1848 to 2007, the maximum amplitude of the next solar cycle is predicted to be about 112 in 2011-2012.

  3. Bayesian estimation of parameters in a regional hydrological model

    Directory of Open Access Journals (Sweden)

    K. Engeland

    2002-01-01

    Full Text Available This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1 process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis

  4. Nonlinear model predictive control using parameter varying BP-ARX combination model

    Science.gov (United States)

    Yang, J.-F.; Xiao, L.-F.; Qian, J.-X.; Li, H.

    2012-03-01

    A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed.

  5. PERFORMANCE OF MULITPLE LINEAR REGRESSION AND NONLINEAR NEURAL NETWORKS AND FUZZY LOGIC TECHNIQUES IN MODELLING HOUSE PRICES

    Directory of Open Access Journals (Sweden)

    Gurudeo Anand Tularam

    2012-01-01

    Full Text Available House price prediction continues to be important for government agencies insurance companies and real estate industry. This study investigates the performance of house sales price models based on linear and non-linear approaches to study the effects of selected variables. Linear stepwise Multivariate Regression (MR and nonlinear models of Neural Network (NN and Adaptive Neuro-Fuzzy (ANFIS are developed and compared. The GIS methods are used to integrate the data for the study area (Bathurst, Australia. While it was expected that the nonlinear methods would be much better the analysis shows NN and ANFIS are only slightly better than MR suggesting questions about high R2 often found in the literature. While structural data and macro-finance variables may contribute to higher R2 performance comparison was the goal of this study and besides the Australian data lacked structural elements. The results show that MR model could be improved. Also, the land value and location explained at best about 45% of the sale price variation. The analysis of price forecasts (within the 10% range of the actual prediction on average revealed that the non-linear models performed slightly better (29% than the linear (26%. The inclusion of social data improves the MR prediction in most of the suburbs. The suburbs analysis shows the importance of socially based locations and also variance due to types of housing dominant. In general terms of R2, the NN model (0.45 performed only slightly better than ANFIS 0.39 and better than MR (0.37; but the linear MRsoc performed better (0.42. In suburb level, the NN model (7/15 performed better than ANFIS (3/15 but the linear MR (5/15 was better than ANFIS. The improved linear MR (6/15 performed nearly as well as the non-linear NN. Linear methods appear to just as precise as the the more time consuming non linear methods in most cases for accounting for the differences and variation. However, when a much more in depth analysis is

  6. A comparison of numerical and machine-learning modeling of soil water content with limited input data

    Science.gov (United States)

    Karandish, Fatemeh; Šimůnek, Jiří

    2016-12-01

    Soil water content (SWC) is a key factor in optimizing the usage of water resources in agriculture since it provides information to make an accurate estimation of crop water demand. Methods for predicting SWC that have simple data requirements are needed to achieve an optimal irrigation schedule, especially for various water-saving irrigation strategies that are required to resolve both food and water security issues under conditions of water shortages. Thus, a two-year field investigation was carried out to provide a dataset to compare the effectiveness of HYDRUS-2D, a physically-based numerical model, with various machine-learning models, including Multiple Linear Regressions (MLR), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Support Vector Machines (SVM), for simulating time series of SWC data under water stress conditions. SWC was monitored using TDRs during the maize growing seasons of 2010 and 2011. Eight combinations of six, simple, independent parameters, including pan evaporation and average air temperature as atmospheric parameters, cumulative growth degree days (cGDD) and crop coefficient (Kc) as crop factors, and water deficit (WD) and irrigation depth (In) as crop stress factors, were adopted for the estimation of SWCs in the machine-learning models. Having Root Mean Square Errors (RMSE) in the range of 0.54-2.07 mm, HYDRUS-2D ranked first for the SWC estimation, while the ANFIS and SVM models with input datasets of cGDD, Kc, WD and In ranked next with RMSEs ranging from 1.27 to 1.9 mm and mean bias errors of -0.07 to 0.27 mm, respectively. However, the MLR models did not perform well for SWC forecasting, mainly due to non-linear changes of SWCs under the irrigation process. The results demonstrated that despite requiring only simple input data, the ANFIS and SVM models could be favorably used for SWC predictions under water stress conditions, especially when there is a lack of data. However, process-based numerical models are undoubtedly a

  7. 基于Neuro-Fuzzy方法的Web服务器访问流量预测%Traffic Prediction of Accessing Web Server Based on Neuro-Fuzzy Method

    Institute of Scientific and Technical Information of China (English)

    周咏梅; 阳爱民

    2004-01-01

    Neuro-Fuzzy方法是将神经网络和模糊逻辑进行有机的结合,用于解决复杂的非线性问题;用它来进行Web服务器流量预测,是一种新的思路和方法.该文介绍了模型构造的基本思想、结构、算法,也介绍了进化式聚类方法和预测过程;同时,给出了实验数据及分析.

  8. Power Quality Improvement Using Custom Power Devices in Squirrel Cage Induction Generator Wind Farm to Weak-Grid Connection by using neuro-fuzzy control

    Directory of Open Access Journals (Sweden)

    Kopella Sai Teja

    2015-02-01

    Full Text Available Wind farm is connected to the grid directly.The wind is not constant voltage fluctuations occur at point of common coupling (PCC and WF terminal . To over come this problem a new compensation strategy is used . By using Custom power devices (UPQC.It injects reactive power at PCC . The advantages of UPQC is it consists of both DVR and D-STATCOM . DVR is connected in series to the line and it injects in phase voltage into the line .D-STATCOM is connected shunt to the line .The internal control strategy is based on management of active and reactive power in series and shunt converters of UPQC . The power exchainge is done by using DC-link

  9. 一种结合神经元网络的PMV模糊算法%A Neuro-Fuzzy Algorithm for Predicted Mean Vote

    Institute of Scientific and Technical Information of China (English)

    李振海; 盖潇筱; 曾东升

    2008-01-01

    针对Fanger的热舒适指标预测平均投票值(PMV)提出了一种结合神经元网络的模糊算法,将PMV的计算划分成5个模糊计算模块,克服了在工程应用中Fanger热舒适方程计算复杂的缺点,各个模块通过神经元网络进行修正以提高算法的精度.将此算法的结果与Fanger的热舒适方程计算结果进行比较,表明此算法精度亦能满足工程需要.

  10. The regional prediction model of PM10 concentrations for Turkey

    Science.gov (United States)

    Güler, Nevin; Güneri İşçi, Öznur

    2016-11-01

    This study is aimed to predict a regional model for weekly PM10 concentrations measured air pollution monitoring stations in Turkey. There are seven geographical regions in Turkey and numerous monitoring stations at each region. Predicting a model conventionally for each monitoring station requires a lot of labor and time and it may lead to degradation in quality of prediction when the number of measurements obtained from any õmonitoring station is small. Besides, prediction models obtained by this way only reflect the air pollutant behavior of a small area. This study uses Fuzzy C-Auto Regressive Model (FCARM) in order to find a prediction model to be reflected the regional behavior of weekly PM10 concentrations. The superiority of FCARM is to have the ability of considering simultaneously PM10 concentrations measured monitoring stations in the specified region. Besides, it also works even if the number of measurements obtained from the monitoring stations is different or small. In order to evaluate the performance of FCARM, FCARM is executed for all regions in Turkey and prediction results are compared to statistical Autoregressive (AR) Models predicted for each station separately. According to Mean Absolute Percentage Error (MAPE) criteria, it is observed that FCARM provides the better predictions with a less number of models.

  11. Specific features of modelling rules of monetary policy on the basis of hybrid regression models with a neural component

    Directory of Open Access Journals (Sweden)

    Lukianenko Iryna H.

    2014-01-01

    Full Text Available The article considers possibilities and specific features of modelling economic phenomena with the help of the category of models that unite elements of econometric regressions and artificial neural networks. This category of models contains auto-regression neural networks (AR-NN, regressions of smooth transition (STR/STAR, multi-mode regressions of smooth transition (MRSTR/MRSTAR and smooth transition regressions with neural coefficients (NCSTR/NCSTAR. Availability of the neural network component allows models of this category achievement of a high empirical authenticity, including reproduction of complex non-linear interrelations. On the other hand, the regression mechanism expands possibilities of interpretation of the obtained results. An example of multi-mode monetary rule is used to show one of the cases of specification and interpretation of this model. In particular, the article models and interprets principles of management of the UAH exchange rate that come into force when economy passes from a relatively stable into a crisis state.

  12. Forecasting the NOK/USD Exchange Rate with Machine Learning Techniques

    OpenAIRE

    Theophilos Papadimitriou; Periklis Gogas; Vasilios Plakandaras

    2013-01-01

    In this paper, we approximate the empirical findings of Papadamou and Markopoulos (2012) on the NOK/USD exchange rate under a Machine Learning (ML) framework. By applying Support Vector Regression (SVR) on a general monetary exchange rate model and a Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS) to extract model structure, we test for the validity of popular monetary exchange rate models. We reach to mixed results since the coefficient sign of interest rate differential is in favor o...

  13. Partitioning and interpolation based hybrid ARIMA–ANN model for time series forecasting

    Indian Academy of Sciences (India)

    C NARENDRA BABU; PALLAVIRAM SURE

    2016-07-01

    Time series data (TSD) originating from different applications have dissimilar characteristics. Hence for prediction of TSD, diversified varieties of prediction models exist. In many applications, hybrid models provide more accurate predictions than individual models. One such hybrid model, namely auto regressive integrated moving average – artificial neural network (ARIMA–ANN) is devised in many different ways in the literature. However, the prediction accuracy of hybrid ARIMA–ANN model can be further improved by devising suitable processing techniques. In this paper, a hybrid ARIMA–ANN model is proposed, which combines the concepts of the recently developed moving average (MA) filter based hybrid ARIMA–ANN model, with a processing technique involving a partitioning–interpolation (PI) step. The improved prediction accuracy of the proposed PI based hybrid ARIMA–ANN model is justified using a simulation experiment.Further, on different experimental TSD like sunspots TSD and electricity price TSD, the proposed hybrid model is applied along with four existing state-of-the-art models and it is found that the proposed model outperforms all the others, and hence is a promising model for TSD prediction

  14. Modelling the effects of (short-term solar variability on stratospheric chemistry

    Directory of Open Access Journals (Sweden)

    R. Muncaster

    2011-12-01

    Full Text Available The photochemical response of the stratosphere to short-term solar variability is investigated using a photochemistry column model with interactive photolysis calculation. The solar variability is here simply represented using the Lean (1997 solar minimum and maximum spectra. In order to isolate the photochemistry effect, simulations are devoid of diffusion or any other external forcing and the temperature is held constant. The solar mininum/maximum response is estimated for all chemical families and partitioning ratios, and the underlying photochemical mechanisms are described in detail. The ozone response peaks at 0.18 ppmv (approximatively 3% at 37 km altitude. In an attempt to find the simplest statistical model able to represent the effect of solar variability in the stratosphere, the diurnal-average response of ozone from an ensemble of 200 simulations is regressed linearly following two auto-regressive models. In the simplest case, an adjusted coefficient of determination R2 larger than 0.97 is found throughout the stratosphere using two predictors, namely the previous day's ozone perturbation and the current day's solar irradiance perturbation. A better accuracy (R2 larger than 0.9992 is achieved with an additional predictor, the previous day's solar irradiance perturbation. The skills of the two auto-regressive models at representing the effect of solar variability are then evaluated independently when coupled either on-line or off-line with the comprehensive photochemistry column model driven by the solar average spectrum. In all cases, the magnitude of the bias and the RMS error are found smaller than 5% and 20% of the ozone response, respectively. When used on-line, the 3-predictor model captures the ozone response to solar variability throughout the stratosphere with bias and RMS error

  15. Comparative analysis of system identification techniques for nonlinear modeling of the neuron-microelectrode junction.

    Science.gov (United States)

    Khan, Saad Ahmad; Thakore, Vaibhav; Behal, Aman; Bölöni, Ladislau; Hickman, James J

    2013-03-01

    Applications of non-invasive neuroelectronic interfacing in the fields of whole-cell biosensing, biological computation and neural prosthetic devices depend critically on an efficient decoding and processing of information retrieved from a neuron-electrode junction. This necessitates development of mathematical models of the neuron-electrode interface that realistically represent the extracellular signals recorded at the neuroelectronic junction without being computationally expensive. Extracellular signals recorded using planar microelectrode or field effect transistor arrays have, until now, primarily been represented using linear equivalent circuit models that fail to reproduce the correct amplitude and shape of the signals recorded at the neuron-microelectrode interface. In this paper, to explore viable alternatives for a computationally inexpensive and efficient modeling of the neuron-electrode junction, input-output data from the neuron-electrode junction is modeled using a parametric Wiener model and a Nonlinear Auto-Regressive network with eXogenous input trained using a dynamic Neural Network model (NARX-NN model). Results corresponding to a validation dataset from these models are then employed to compare and contrast the computational complexity and efficiency of the aforementioned modeling techniques with the Lee-Schetzen technique of cross-correlation for estimating a nonlinear dynamic model of the neuroelectronic junction.

  16. Development of Aeroservoelastic Analytical Models and Gust Load Alleviation Control Laws of a SensorCraft Wind-Tunnel Model Using Measured Data

    Science.gov (United States)

    Silva, Walter A.; Vartio, Eric; Shimko, Anthony; Kvaternik, Raymond G.; Eure, Kenneth W.; Scott,Robert C.

    2007-01-01

    Aeroservoelastic (ASE) analytical models of a SensorCraft wind-tunnel model are generated using measured data. The data was acquired during the ASE wind-tunnel test of the HiLDA (High Lift-to-Drag Active) Wing model, tested in the NASA Langley Transonic Dynamics Tunnel (TDT) in late 2004. Two time-domain system identification techniques are applied to the development of the ASE analytical models: impulse response (IR) method and the Generalized Predictive Control (GPC) method. Using measured control surface inputs (frequency sweeps) and associated sensor responses, the IR method is used to extract corresponding input/output impulse response pairs. These impulse responses are then transformed into state-space models for use in ASE analyses. Similarly, the GPC method transforms measured random control surface inputs and associated sensor responses into an AutoRegressive with eXogenous input (ARX) model. The ARX model is then used to develop the gust load alleviation (GLA) control law. For the IR method, comparison of measured with simulated responses are presented to investigate the accuracy of the ASE analytical models developed. For the GPC method, comparison of simulated open-loop and closed-loop (GLA) time histories are presented.

  17. 模糊系统

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    [ 篇名] A Fuzzy Additive Reasoning Scheme for Probabilistic Mamdani Fuzzy Systems,[ 篇名] A fuzzy linguistic-based software tool for seismic image interpretation,[ 篇名 ]A Fuzzy Model of Support Vector Machine Regression,[ 篇名 ] A fuzzy system modelling of the evolution of tumour in human tissues,[篇名 ] A generalised approach to point-to-point motion of multi-DOF parallel manipulators illustrated with TMPM,[篇名] A hierarchical fuzzy steering controller for mobile robots,[ 篇名 ] A Hierarchical Neuro-Fiizzy System Based on S-Implications,[ 篇名]A HIERARCHICAL NEURO-FUZZY SYSTEM FOR IDENTIFICATION OF SIMULTANEOUS FAULTS IN HYDRAULIC SERVOVALVES,[ 篇名 ] A Hybrid Two-Stage Fuzzy ARTMAP and LVQ Neuro-Fuzzy System for On-Line Handwriting Recognition。

  18. A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution.

    Science.gov (United States)

    Lee, Duncan; Rushworth, Alastair; Sahu, Sujit K

    2014-06-01

    Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models.

  19. Distributed parameter system coupled ARMA expansion identification and adaptive parallel IIR filtering - A unified problem statement. [Auto Regressive Moving-Average

    Science.gov (United States)

    Johnson, C. R., Jr.; Balas, M. J.

    1980-01-01

    A novel interconnection of distributed parameter system (DPS) identification and adaptive filtering is presented, which culminates in a common statement of coupled autoregressive, moving-average expansion or parallel infinite impulse response configuration adaptive parameterization. The common restricted complexity filter objectives are seen as similar to the reduced-order requirements of the DPS expansion description. The interconnection presents the possibility of an exchange of problem formulations and solution approaches not yet easily addressed in the common finite dimensional lumped-parameter system context. It is concluded that the shared problems raised are nevertheless many and difficult.

  20. A comparative study of artificial intelligent-based maximum power point tracking for photovoltaic systems

    Science.gov (United States)

    Hussain Mutlag, Ammar; Mohamed, Azah; Shareef, Hussain

    2016-03-01

    Maximum power point tracking (MPPT) is normally required to improve the performance of photovoltaic (PV) systems. This paper presents artificial intelligent-based maximum power point tracking (AI-MPPT) by considering three artificial intelligent techniques, namely, artificial neural network (ANN), adaptive neuro fuzzy inference system with seven triangular fuzzy sets (7-tri), and adaptive neuro fuzzy inference system with seven gbell fuzzy sets. The AI-MPPT is designed for the 25 SolarTIFSTF-120P6 PV panels, with the capacity of 3 kW peak. A complete PV system is modelled using 300,000 data samples and simulated in the MATLAB/SIMULINK. The AI-MPPT has been tested under real environmental conditions for two days from 8 am to 18 pm. The results showed that the ANN based MPPT gives the most accurate performance and then followed by the 7-tri-based MPPT.

  1. The effect of seasonal variation on the performances of grid connected photovoltaic system in southern of Algeria

    Science.gov (United States)

    Zaghba, L.; Khennane, M.; Terki, N.; Borni, A.; Bouchakour, A.; Fezzani, A.; Mahamed, I. Hadj; Oudjana, S. H.

    2017-02-01

    This paper presents modeling, simulation, and analysis evaluation of the grid-connected PV generation system performance under MATLAB/Simulink. The objective is to study the effect of seasonal variation on the performances of grid connected photovoltaic system in southern of Algeria. This system works with a power converter. This converter allows the connection to the network and extracts maximum power from photovoltaic panels with the MPPT algorithm based on robust neuro-fuzzy sliding approach. The photovoltaic energy produced by the PV generator will be completely injected on the network. Simulation results show that the system controlled by the neuro-fuzzy sliding adapts to changing external disturbances and show their effectiveness not only for continued maximum power point but also for response time and stability.

  2. Distributed intrusion detection system based on fuzzy rules

    Science.gov (United States)

    Qiao, Peili; Su, Jie; Liu, Yahui

    2006-04-01

    Computational Intelligence is the theory and method solving problems by simulating the intelligence of human using computer and it is the development of Artificial Intelligence. Fuzzy Technique is one of the most important theories of computational Intelligence. Genetic Fuzzy Technique and Neuro-Fuzzy Technique are the combination of Fuzzy Technique and novel techniques. This paper gives a distributed intrusion detection system based on fuzzy rules that has the characters of distributed parallel processing, self-organization, self-learning and self-adaptation by the using of Neuro-Fuzzy Technique and Genetic Fuzzy Technique. Specially, fuzzy decision technique can be used to reduce false detection. The results of the simulation experiment show that this intrusion detection system model has the characteristics of distributed, error tolerance, dynamic learning, and adaptation. It solves the problem of low identifying rate to new attacks and hidden attacks. The false detection rate is low. This approach is efficient to the distributed intrusion detection.

  3. Modeling tropical river runoff:A time dependent approach

    Institute of Scientific and Technical Information of China (English)

    Rashmi Nigam; Sudhir Nigam; Sushil K.Mittal

    2014-01-01

    Forecasting of rainfall and subsequent river runoff is important for many operational problems and applications related to hydrol-ogy. Modeling river runoff often requires rigorous mathematical analysis of vast historical data to arrive at reasonable conclusions. In this paper we have applied the stochastic method to characterize and predict river runoff of the perennial Kulfo River in south-ern Ethiopia. The time series analysis based auto regressive integrated moving average (ARIMA) approach is applied to mean monthly runoff data with 10 and 20 years spans. The varying length of the input runoff data is shown to influence the forecasting efficiency of the stochastic process. Preprocessing of the runoff time series data indicated that the data do not follow a seasonal pattern. Our forecasts were made using parsimonious non seasonal ARIMA models and the results were compared to actual 10-year and 20-year mean monthly runoff data of the Kulfo River. Our results indicate that river runoff forecasts based upon the 10-year data are more accurate and efficient than the model based on the 20-year time series.

  4. Dynamic Flow Modeling Using Double POD and ANN-ARX System Identification

    Science.gov (United States)

    Siegel, Stefan; Seidel, Jürgen; Cohen, Kelly; Aradag, Selin; McLaughlin, Thomas

    2007-11-01

    Double Proper Orthogonal Decomposition (DPOD), a modification of conventional POD, is a powerful tool for modeling of transient flow field spatial features, in particular, a 2D cylinder wake at a Reynolds number of 100. To develop a model for control design, the interaction of DPOD mode amplitudes with open-loop control inputs needs to be captured. Traditionally, Galerkin projection onto the Navier Stokes equations has been used for that purpose. Given the stability problems as well as issues in correctly modeling actuation input, we propose a different approach. We demonstrate that the ARX (Auto Regressive eXternal input) system identification method in connection with an Artificial Neural Network (ANN) nonlinear structure leads to a model that captures the dynamic behavior of the unforced and transient forced open loop data used for model development. Moreover, we also show that the model is valid at different Reynolds numbers, for different open loop forcing parameters, as well as for closed loop flow states with excellent accuracy. Thus, we present with this DPOD-ANN-ARX model a paradigm shift for laminar circular cylinder wake modeling that is proven valid for feedback flow controller development.

  5. Hybrid Adaptive Filter development for the minimisation of transient fluctuations superimposed on electrotelluric field recordings mainly by magnetic storms

    Directory of Open Access Journals (Sweden)

    A. Konstantaras

    2006-01-01

    Full Text Available The method of Hybrid Adaptive Filtering (HAF aims to recover the recorded electric field signals from anomalies of magnetotelluric origin induced mainly by magnetic storms. An adaptive filter incorporating neuro-fuzzy technology has been developed to remove any significant distortions from the equivalent magnetic field signal, as retrieved from the original electric field signal by reversing the magnetotelluric method. Testing with further unseen data verifies the reliability of the model and demonstrates the effectiveness of the HAF method.

  6. Nonlinear modeling and identification of the electro-hydraulic control system of an excavator arm using BONL model

    Science.gov (United States)

    Yan, Jun; Li, Bo; Guo, Gang; Zeng, Yonghua; Zhang, Meijun

    2013-11-01

    Electro-hydraulic control systems are nonlinear in nature and their mathematic models have unknown parameters. Existing research of modeling and identification of the electro-hydraulic control system is mainly based on theoretical state space model, and the parameters identification is hard due to its demand on internal states measurement. Moreover, there are also some hard-to-model nonlinearities in theoretical model, which needs to be overcome. Modeling and identification of the electro-hydraulic control system of an excavator arm based on block-oriented nonlinear(BONL) models is investigated. The nonlinear state space model of the system is built first, and field tests are carried out to reveal the nonlinear characteristics of the system. Based on the physic insight into the system, three BONL models are adopted to describe the highly nonlinear system. The Hammerstein model is composed of a two-segment polynomial nonlinearity followed by a linear dynamic subsystem. The Hammerstein-Wiener(H-W) model is represented by the Hammerstein model in cascade with another single polynomial nonlinearity. A novel Pseudo-Hammerstein-Wiener(P-H-W) model is developed by replacing the single polynomial of the H-W model by a non-smooth backlash function. The key term separation principle is applied to simplify the BONL models into linear-in-parameters structures. Then, a modified recursive least square algorithm(MRLSA) with iterative estimation of internal variables is developed to identify the all the parameters simultaneously. The identification results demonstrate that the BONL models with two-segment polynomial nonlinearities are able to capture the system behavior, and the P-H-W model has the best prediction accuracy. Comparison experiments show that the velocity prediction error of the P-H-W model is reduced by 14%, 30% and 75% to the H-W model, Hammerstein model, and extended auto-regressive (ARX) model, respectively. This research is helpful in controller design, system

  7. Effect of climate variables on cocoa black pod incidence in Sabah using ARIMAX model

    Science.gov (United States)

    Ling Sheng Chang, Albert; Ramba, Haya; Mohd. Jaaffar, Ahmad Kamil; Kim Phin, Chong; Chong Mun, Ho

    2016-06-01

    Cocoa black pod disease is one of the major diseases affecting the cocoa production in Malaysia and also around the world. Studies have shown that the climate variables have influenced the cocoa black pod disease incidence and it is important to quantify the black pod disease variation due to the effect of climate variables. Application of time series analysis especially auto-regressive moving average (ARIMA) model has been widely used in economics study and can be used to quantify the effect of climate variables on black pod incidence to forecast the right time to control the incidence. However, ARIMA model does not capture some turning points in cocoa black pod incidence. In order to improve forecasting performance, other explanatory variables such as climate variables should be included into ARIMA model as ARIMAX model. Therefore, this paper is to study the effect of climate variables on the cocoa black pod disease incidence using ARIMAX model. The findings of the study showed ARIMAX model using MA(1) and relative humidity at lag 7 days, RHt - 7 gave better R square value compared to ARIMA model using MA(1) which could be used to forecast the black pod incidence to assist the farmers determine timely application of fungicide spraying and culture practices to control the black pod incidence.

  8. ARIMA MODEL BUILDING AND FORECASTING ON IMPORTS AND EXPORTS OF PAKISTAN

    Directory of Open Access Journals (Sweden)

    Ahmad Farooqi

    2014-08-01

    Full Text Available From the day one, mankind has always been interested in to the future. As the civilization advanced with growing sophistication in all phases of life, the need to look in to the future also grew with it. Today every government, public private organizations, as well as an individual would like to predict and plan for the future. In order to attain a better growth in the economy of a country, modeling and forecasting is the most important tool now a day, this can be done by one of the statistical technique called a Time series analysis. In this paper we tried to build a time series model called ARIMA (Auto Regressive Integrated Moving Average model with particular reference of Box and Jenkins approach on annually total Imports and Exports of Pakistan from the year 1947 to the year 2013 with useful statistical software R. Validity of the fitted model is tested using standard statistical techniques. The fitted model is then use to forecast some future values of Imports and export of Pakistan. It is found that an ARIMA (2, 2, 2 and ARIMA (1, 2, 2 model looks suitable to forecast the annual Imports and Exports of Pakistan respectively. We also found an increasing trend both in case of Imports and Exports during this study.

  9. Machine Learning in Parliament Elections

    Directory of Open Access Journals (Sweden)

    Ahmad Esfandiari

    2012-09-01

    Full Text Available Parliament is considered as one of the most important pillars of the country governance. The parliamentary elections and prediction it, had been considered by scholars of from various field like political science long ago. Some important features are used to model the results of consultative parliament elections. These features are as follows: reputation and popularity, political orientation, tradesmen's support, clergymen's support, support from political wings and the type of supportive wing. Two parameters of reputation and popularity and the support of clergymen and religious scholars that have more impact in reducing of prediction error in election results, have been used as input parameters in implementation. In this study, the Iranian parliamentary elections, modeled and predicted using learnable machines of neural network and neuro-fuzzy. Neuro-fuzzy machine combines the ability of knowledge representation of fuzzy sets and the learning power of neural networks simultaneously. In predicting the social and political behavior, the neural network is first trained by two learning algorithms using the training data set and then this machine predict the result on test data. Next, the learning of neuro-fuzzy inference machine is performed. Then, be compared the results of two machines.

  10. Model parameter estimation approach based on incremental analysis for lithium-ion batteries without using open circuit voltage

    Science.gov (United States)

    Wu, Hongjie; Yuan, Shifei; Zhang, Xi; Yin, Chengliang; Ma, Xuerui

    2015-08-01

    To improve the suitability of lithium-ion battery model under varying scenarios, such as fluctuating temperature and SoC variation, dynamic model with parameters updated realtime should be developed. In this paper, an incremental analysis-based auto regressive exogenous (I-ARX) modeling method is proposed to eliminate the modeling error caused by the OCV effect and improve the accuracy of parameter estimation. Then, its numerical stability, modeling error, and parametric sensitivity are analyzed at different sampling rates (0.02, 0.1, 0.5 and 1 s). To identify the model parameters recursively, a bias-correction recursive least squares (CRLS) algorithm is applied. Finally, the pseudo random binary sequence (PRBS) and urban dynamic driving sequences (UDDSs) profiles are performed to verify the realtime performance and robustness of the newly proposed model and algorithm. Different sampling rates (1 Hz and 10 Hz) and multiple temperature points (5, 25, and 45 °C) are covered in our experiments. The experimental and simulation results indicate that the proposed I-ARX model can present high accuracy and suitability for parameter identification without using open circuit voltage.

  11. ARX-NNPLS Model Based Optimization Strategy and Its Application in Polymer Grade Transition Process

    Institute of Scientific and Technical Information of China (English)

    费正顺; 胡斌; 叶鲁彬; 梁军

    2012-01-01

    Since it is often difficult to build differential algebraic equations (DAEs) for chemical processes, a new data-based modeling approach is proposed using ARX (AutoRegressive with eXogenous inputs) combined with neural network under partial least squares framework (ARX-NNPLS), in which less specific knowledge of the process is required but the input and output data. To represent the dynamic and nonlinear behavior of the process, the ARX combined with neural network is used in the partial least squares (PLS) inner model between input and output latent variables. In the proposed dynamic optimization strategy based on the ARX-NNPLS model, neither parameterization nor iterative solving process for DAEs is needed as the ARX-NNPLS model gives a proper representation for the dynamic behavior of the process, and the computing time is greatly reduced compared to conventional control vector parameterization method. To demonstrate the ARX-NNPLS model based optimization strategy, the polyethylene grade transition in gas phase fluidized-bed reactor is taken into account. The optimization results show that the final optimal trajectory of quality index determined by the new approach moves faster to the target values and the computing time is much less.

  12. Principal dynamic mode analysis of neural mass model for the identification of epileptic states.

    Science.gov (United States)

    Cao, Yuzhen; Jin, Liu; Su, Fei; Wang, Jiang; Deng, Bin

    2016-11-01

    The detection of epileptic seizures in Electroencephalography (EEG) signals is significant for the diagnosis and treatment of epilepsy. In this paper, in order to obtain characteristics of various epileptiform EEGs that may differentiate different states of epilepsy, the concept of Principal Dynamic Modes (PDMs) was incorporated to an autoregressive model framework. First, the neural mass model was used to simulate the required intracerebral EEG signals of various epileptiform activities. Then, the PDMs estimated from the nonlinear autoregressive Volterra models, as well as the corresponding Associated Nonlinear Functions (ANFs), were used for the modeling of epileptic EEGs. The efficient PDM modeling approach provided physiological interpretation of the system. Results revealed that the ANFs of the 1st and 2nd PDMs for the auto-regressive input exhibited evident differences among different states of epilepsy, where the ANFs of the sustained spikes' activity encountered at seizure onset or during a seizure were the most differentiable from that of the normal state. Therefore, the ANFs may be characteristics for the classification of normal and seizure states in the clinical detection of seizures and thus provide assistance for the diagnosis of epilepsy.

  13. Modeling and forecasting livestock feed resources in India using climate variables.

    Science.gov (United States)

    Suresh, K P; Kiran, G Ravi; Giridhar, K; Sampath, K T

    2012-04-01

    The availability and efficient use of the feed resources in India are the primary drivers to maximize productivity of Indian livestock. Feed security is vital to the livestock management, extent of use, conservation and productivity enhancement. Assessment and forecasting of livestock feed resources are most important for effective planning and policy making. In the present study, 40 years of data on crop production, land use pattern, rainfall, its deviation from normal, area under crop and yield of crop were collected and modeled to forecast the likely production of feed resources for the next 20 years. The higher order auto-regressive (AR) models were used to develop efficient forecasting models. Use of climatic variables (actual rainfall and its deviation from normal) in combination with non-climatic factors like area under each crop, yield of crop, lag period etc., increased the efficiency of forecasting models. From the best fitting models, the current total dry matter (DM) availability in India was estimated to be 510.6 million tonnes (mt) comprising of 47.2 mt from concentrates, 319.6 mt from crop residues and 143.8 mt from greens. The availability of DM from dry fodder, green fodder and concentrates is forecasted at 409.4, 135.6 and 61.2 mt, respectively, for 2030.

  14. Detection of antipersonnel (AP) mines using mechatronics approach

    Science.gov (United States)

    Shahri, Ali M.; Naghdy, Fazel

    1998-09-01

    At present there are approximately 110 million land-mines scattered around the world in 64 countries. The clearance of these mines takes place manually. Unfortunately, on average for every 5000 mines cleared one mine clearer is killed. A Mine Detector Arm (MDA) using mechatronics approach is under development in this work. The robot arm imitates manual hand- prodding technique for mine detection. It inserts a bayonet into the soil and models the dynamics of the manipulator and environment parameters, such as stiffness variation in the soil to control the impact caused by contacting a stiff object. An explicit impact control scheme is applied as the main control scheme, while two different intelligent control methods are designed to deal with uncertainties and varying environmental parameters. Firstly, a neuro-fuzzy adaptive gain controller (NFAGC) is designed to adapt the force gain control according to the estimated environment stiffness. Then, an adaptive neuro-fuzzy plus PID controller is employed to switch from a conventional PID controller to neuro-fuzzy impact control (NFIC), when an impact is detected. The developed control schemes are validated through computer simulation and experimental work.

  15. Studying dynamic social processes with ARIMA modeling

    NARCIS (Netherlands)

    Vasileiadou, E.; Vliegenthart, R.

    2014-01-01

    With the increasing use of information and communication technologies, there is a wealth of longitudinal data available, which open up new research directions. This availability necessitates special analytical tools, namely time series analysis methods. The paper focuses on Auto Regressive Integrate

  16. Comparison of Conventional and ANN Models for River Flow Forecasting

    Science.gov (United States)

    Jain, A.; Ganti, R.

    2011-12-01

    Hydrological models are useful in many water resources applications such as flood control, irrigation and drainage, hydro power generation, water supply, erosion and sediment control, etc. Estimates of runoff are needed in many water resources planning, design development, operation and maintenance activities. River flow is generally estimated using time series or rainfall-runoff models. Recently, soft artificial intelligence tools such as Artificial Neural Networks (ANNs) have become popular for research purposes but have not been extensively adopted in operational hydrological forecasts. There is a strong need to develop ANN models based on real catchment data and compare them with the conventional models. In this paper, a comparative study has been carried out for river flow forecasting using the conventional and ANN models. Among the conventional models, multiple linear, and non linear regression, and time series models of auto regressive (AR) type have been developed. Feed forward neural network model structure trained using the back propagation algorithm, a gradient search method, was adopted. The daily river flow data derived from Godavari Basin @ Polavaram, Andhra Pradesh, India have been employed to develop all the models included here. Two inputs, flows at two past time steps, (Q(t-1) and Q(t-2)) were selected using partial auto correlation analysis for forecasting flow at time t, Q(t). A wide range of error statistics have been used to evaluate the performance of all the models developed in this study. It has been found that the regression and AR models performed comparably, and the ANN model performed the best amongst all the models investigated in this study. It is concluded that ANN model should be adopted in real catchments for hydrological modeling and forecasting.

  17. Generalized versus non-generalized neural network model for multi-lead inflow forecasting at Aswan High Dam

    Directory of Open Access Journals (Sweden)

    A. El-Shafie

    2011-03-01

    Full Text Available Artificial neural networks (ANN have been found efficient, particularly in problems where characteristics of the processes are stochastic and difficult to describe using explicit mathematical models. However, time series prediction based on ANN algorithms is fundamentally difficult and faces problems. One of the major shortcomings is the search for the optimal input pattern in order to enhance the forecasting capabilities for the output. The second challenge is the over-fitting problem during the training procedure and this occurs when ANN loses its generalization. In this research, autocorrelation and cross correlation analyses are suggested as a method for searching the optimal input pattern. On the other hand, two generalized methods namely, Regularized Neural Network (RNN and Ensemble Neural Network (ENN models are developed to overcome the drawbacks of classical ANN models. Using Generalized Neural Network (GNN helped avoid over-fitting of training data which was observed as a limitation of classical ANN models. Real inflow data collected over the last 130 years at Lake Nasser was used to train, test and validate the proposed model. Results show that the proposed GNN model outperforms non-generalized neural network and conventional auto-regressive models and it could provide accurate inflow forecasting.

  18. Hierarchical Bayesian Markov switching models with application to predicting spawning success of shovelnose sturgeon

    Science.gov (United States)

    Holan, S.H.; Davis, G.M.; Wildhaber, M.L.; DeLonay, A.J.; Papoulias, D.M.

    2009-01-01

    The timing of spawning in fish is tightly linked to environmental factors; however, these factors are not very well understood for many species. Specifically, little information is available to guide recruitment efforts for endangered species such as the sturgeon. Therefore, we propose a Bayesian hierarchical model for predicting the success of spawning of the shovelnose sturgeon which uses both biological and behavioural (longitudinal) data. In particular, we use data that were produced from a tracking study that was conducted in the Lower Missouri River. The data that were produced from this study consist of biological variables associated with readiness to spawn along with longitudinal behavioural data collected by using telemetry and archival data storage tags. These high frequency data are complex both biologically and in the underlying behavioural process. To accommodate such complexity we developed a hierarchical linear regression model that uses an eigenvalue predictor, derived from the transition probability matrix of a two-state Markov switching model with generalized auto-regressive conditional heteroscedastic dynamics. Finally, to minimize the computational burden that is associated with estimation of this model, a parallel computing approach is proposed. ?? Journal compilation 2009 Royal Statistical Society.

  19. Constrained generalized predictive control of battery charging process based on a coupled thermoelectric model

    Science.gov (United States)

    Liu, Kailong; Li, Kang; Zhang, Cheng

    2017-04-01

    Battery temperature is a primary factor affecting the battery performance, and suitable battery temperature control in particular internal temperature control can not only guarantee battery safety but also improve its efficiency. This is however challenging as current controller designs for battery charging have no mechanisms to incorporate such information. This paper proposes a novel battery charging control strategy which applies the constrained generalized predictive control (GPC) to charge a LiFePO4 battery based on a newly developed coupled thermoelectric model. The control target primarily aims to maintain the battery cell internal temperature within a desirable range while delivering fast charging. To achieve this, the coupled thermoelectric model is firstly introduced to capture the battery behaviours in particular SOC and internal temperature which are not directly measurable in practice. Then a controlled auto-regressive integrated moving average (CARIMA) model whose parameters are identified by the recursive least squares (RLS) algorithm is developed as an online self-tuning predictive model for a GPC controller. Then the constrained generalized predictive controller is developed to control the charging current. Experiment results confirm the effectiveness of the proposed control strategy. Further, the best region of heat dissipation rate and proper internal temperature set-points are also investigated and analysed.

  20. A Regularized SNPOM for Stable Parameter Estimation of RBF-AR(X) Model.

    Science.gov (United States)

    Zeng, Xiaoyong; Peng, Hui; Zhou, Feng

    2017-01-20

    Recently, the radial basis function (RBF) network-style coefficients AutoRegressive (with exogenous inputs) [RBF-AR(X)] model identified by the structured nonlinear parameter optimization method (SNPOM) has attracted considerable interest because of its significant performance in nonlinear system modeling. However, this promising technique may occasionally confront the problem that the parameters are divergent in the optimization process, which may be a potential issue ignored by most researchers. In this paper, a regularized SNPOM, together with the regularization parameter detection technique, is presented to estimate the parameters of RBF-AR(X) models. This approach first separates the parameters of an RBF-AR(X) model into a linear parameters set and a nonlinear parameters set, and then combines a gradient-based nonlinear optimization algorithm for estimating the nonlinear parameters and the regularized least squares method for estimating the linear parameters. Several examples demonstrate that the proposed approach is effective to cope with the potential unstable problem in the parameters search process, and may also yield better or similar multistep forecasting accuracy and better robustness than the previous method.

  1. Hybrid Wind Speed Prediction Based on a Self-Adaptive ARIMAX Model with an Exogenous WRF Simulation

    Directory of Open Access Journals (Sweden)

    Erdong Zhao

    2015-12-01

    Full Text Available Wind speed forecasting is difficult not only because of the influence of atmospheric dynamics but also for the impossibility of providing an accurate prediction with traditional statistical forecasting models that work by discovering an inner relationship within historical records. This paper develops a self-adaptive (SA auto-regressive integrated moving average with exogenous variables (ARIMAX model that is optimized very-short-term by the chaotic particle swarm optimization (CPSO algorithm, known as the SA-ARIMA-CPSO approach, for wind speed prediction. The ARIMAX model chooses the wind speed result from the Weather Research and Forecasting (WRF simulation as an exogenous input variable. Further, an SA strategy is applied to the ARIMAX process. When new information is available, the model process can be updated adaptively with parameters optimized by the CPSO algorithm. The proposed SA-ARIMA-CPSO approach enables the forecasting process to update training information and model parameters intelligently and adaptively. As tested using the 15-min wind speed data collected from a wind farm in Northern China, the improved method has the best performance compared with several other models.

  2. State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model

    Directory of Open Access Journals (Sweden)

    Hongjie Wu

    2013-01-01

    Full Text Available State of charge (SOC is a critical factor to guarantee that a battery system is operating in a safe and reliable manner. Many uncertainties and noises, such as fluctuating current, sensor measurement accuracy and bias, temperature effects, calibration errors or even sensor failure, etc. pose a challenge to the accurate estimation of SOC in real applications. This paper adds two contributions to the existing literature. First, the auto regressive exogenous (ARX model is proposed here to simulate the battery nonlinear dynamics. Due to its discrete form and ease of implemention, this straightforward approach could be more suitable for real applications. Second, its order selection principle and parameter identification method is illustrated in detail in this paper. The hybrid pulse power characterization (HPPC cycles are implemented on the 60AH LiFePO4 battery module for the model identification and validation. Based on the proposed ARX model, SOC estimation is pursued using the extended Kalman filter. Evaluation of the adaptability of the battery models and robustness of the SOC estimation algorithm are also verified. The results indicate that the SOC estimation method using the Kalman filter based on the ARX model shows great performance. It increases the model output voltage accuracy, thereby having the potential to be used in real applications, such as EVs and HEVs.

  3. Stochastic modeling of empirical time series of childhood infectious diseases data before and after mass vaccination

    Directory of Open Access Journals (Sweden)

    Trottier Helen

    2006-08-01

    Full Text Available Abstract The goal of this paper is to analyze the stochastic dynamics of childhood infectious disease time series. We present an univariate time series analysis of pertussis, mumps, measles and rubella based on Box-Jenkins or AutoRegressive Integrated Moving Average (ARIMA modeling. The method, which enables the dependency structure embedded in time series data to be modeled, has potential research applications in studies of infectious disease dynamics. Canadian chronological series of pertussis, mumps, measles and rubella, before and after mass vaccination, are analyzed to characterize the statistical structure of these diseases. Despite the fact that these infectious diseases are biologically different, it is found that they are all represented by simple models with the same basic statistical structure. Aside from seasonal effects, the number of new cases is given by the incidence in the previous period and by periodically recurrent random factors. It is also shown that mass vaccination does not change this stochastic dependency. We conclude that the Box-Jenkins methodology does identify the collective pattern of the dynamics, but not the specifics of the diseases at the biological individual level.

  4. FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.

    Science.gov (United States)

    Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao

    2016-07-12

    In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

  5. Modelo logístico difásico no estudo do crescimento de fêmeas da raça Hereford Difasics logistic model in the study of the growth of Hereford breed females

    Directory of Open Access Journals (Sweden)

    Patrícia Neves Mendes

    2008-10-01

    Full Text Available Este trabalho teve como objetivo comparar modelos logísticos difásicos ponderados aplicados ao estudo de curvas de crescimento de fêmeas Hereford com três diferentes estruturas de erros: erros independentes (EI, auto-regressivos de primeira ordem (AR (1 e auto-regressivo de segunda ordem (AR (2 a dados de peso-idade de 55 fêmeas da raça Hereford avaliadas desde o nascimento até 675 dias de idade. Utilizou-se o procedimento model do software Statistical Analysis System (SAS por meio das opções weight e %AR. A comparação entre os modelos foi realizada com base na interpretação biológica dos parâmetros e nos avaliadores de qualidade de ajuste (coeficiente de determinação ajustado, teste de Durbin-Watson, desvio padrão residual, número de iterações, além do critério de informação de Akaike (AIC e do teste F para comparação de modelos. Os resultados obtidos para o ajuste dos modelos aos dados médios indicaram que o modelo logístico difásico AR (2 foi o mais eficiente para descrever a curva de crescimento do rebanho. Ao se considerar o conjunto de dados individuais, nenhum dos modelos abordados foi recomendado por produzirem estimativas não condizentes com a realidade.This study had the objective of comparing weighted difasics logistic models applied to the study of Hereford females growth curves with three different error structures: independent errors (IE, first-order auto-regressive (AR (1 and second-order auto-regressive (AR (2 to weight-age data of 55 females of the Hereford breed, raised in the Bagé region, RS, Brazil, evaluated from birth to 675 days old. The weight and %AR options of model procedure, available in the software Statistical Analysis System (SAS, was used to fit data. The comparison among the models was carried out through the biological interpretation basis of the parameters and in the adjustment of quality measures (adjusted determination coefficient, Durbin-Watson test, residual standard

  6. Development, Verification and Use of Gust Modeling in the NASA Computational Fluid Dynamics Code FUN3D

    Science.gov (United States)

    Bartels, Robert E.

    2012-01-01

    This paper presents the implementation of gust modeling capability in the CFD code FUN3D. The gust capability is verified by computing the response of an airfoil to a sharp edged gust. This result is compared with the theoretical result. The present simulations will be compared with other CFD gust simulations. This paper also serves as a users manual for FUN3D gust analyses using a variety of gust profiles. Finally, the development of an Auto-Regressive Moving-Average (ARMA) reduced order gust model using a gust with a Gaussian profile in the FUN3D code is presented. ARMA simulated results of a sequence of one-minus-cosine gusts is shown to compare well with the same gust profile computed with FUN3D. Proper Orthogonal Decomposition (POD) is combined with the ARMA modeling technique to predict the time varying pressure coefficient increment distribution due to a novel gust profile. The aeroelastic response of a pitch/plunge airfoil to a gust environment is computed with a reduced order model, and compared with a direct simulation of the system in the FUN3D code. The two results are found to agree very well.

  7. Modelling discontinuous well log signal to identify lithological boundaries via wavelet analysis: An example from KTB borehole data

    Indian Academy of Sciences (India)

    Amrita Singh; Saumen Maiti; R K Tiwar

    2016-06-01

    Identification of sharp and discontinuous lithological boundaries from well log signal stemming fromheterogeneous subsurface structures assumes a special significance in geo-exploration studies. Well logdata acquired from various geological settings generally display nonstationary/nonlinear characteristicswith varying wavelengths and frequencies. Modelling of such complex well-log signals using the conventionalsignal processing techniques either fails to catch-up abrupt boundaries or at the best, do notprovide precise information on insidious lithological discontinuities. In this paper, we have proposed anew wavelet transform-based algorithm to model the abrupt discontinuous changes from well log databy taking care of nonstationary characteristics of the signal. Prior to applying the algorithm on thegeophysical well data, we analyzed the distribution of wavelet coefficients using synthetic signal generatedby the first order nonstationary auto-regressive model and then applied the method on actual welllog dataset obtained from the KTB bore hole, Germany. Besides identifying the formation of layeredboundaries, the underlying method also maps some additional formation boundaries, which were hithertoundetected at the KTB site. The results match well with known geological lithostratigraphy andwill be useful for constraining the future model of KTB bore hole data.

  8. Multi-fidelity modelling via recursive co-kriging and Gaussian–Markov random fields

    Science.gov (United States)

    Perdikaris, P.; Venturi, D.; Royset, J. O.; Karniadakis, G. E.

    2015-01-01

    We propose a new framework for design under uncertainty based on stochastic computer simulations and multi-level recursive co-kriging. The proposed methodology simultaneously takes into account multi-fidelity in models, such as direct numerical simulations versus empirical formulae, as well as multi-fidelity in the probability space (e.g. sparse grids versus tensor product multi-element probabilistic collocation). We are able to construct response surfaces of complex dynamical systems by blending multiple information sources via auto-regressive stochastic modelling. A computationally efficient machine learning framework is developed based on multi-level recursive co-kriging with sparse precision matrices of Gaussian–Markov random fields. The effectiveness of the new algorithms is demonstrated in numerical examples involving a prototype problem in risk-averse design, regression of random functions, as well as uncertainty quantification in fluid mechanics involving the evolution of a Burgers equation from a random initial state, and random laminar wakes behind circular cylinders. PMID:26345079

  9. The study on Sanmenxia annual flow forecasting in the Yellow River with mix regression model

    Institute of Scientific and Technical Information of China (English)

    JIANG Xiaohui; LIU Changming; WANG Yu; WANG Hongrui

    2004-01-01

    This paper established mix regression model for simulating annual flow, in which annual runoff is auto-regression factor, precipitation, air temperature and water consumption are regression factors; we adopted 9 hypothesis climate change schemes to forecast the change of annual flow of Sanmenxia Station. The results show: (1) When temperature is steady, the average annual runoff will increase by 8.3% if precipitation increases by 10%; when precipitation decreases by 10%, the average annual runoff will decrease by 8.2%; when precipitation is steady, the average annual runoff will decrease by 2.4% if temperature increases 1 ℃; if temperature decreases 1 ℃, runoff will increase by 1.2%. The mix regression model can well simulate annual runoff. (2) As to 9 different temperature and precipitation scenarios, scenario 9 is the most adverse to the runoff of Sanmenxia Station of Yellow River; i.e. temperature increases 1℃and precipitation decreases by 10%. Under this condition, the simulated average annual runoff decreases by 10.8%. On the contrary, scenario 1 is the best to the enhancement of runoff; i.e. when temperature decreases 1 ℃ precipitation will increase by 10%, which will make the annual runoff of Sanmenxia increase by 10.6%.

  10. Application of ARMAV models to the identification and damage detection of mechanical and civil engineering structures

    Science.gov (United States)

    Bodeux, J. B.; Golinval, J. C.

    2001-06-01

    In this paper, the application of auto-regressive moving average vector models to system identification and damage detection is investigated. These parametric models have already been applied for the analysis of multiple input-output systems under ambient excitation. Their main advantage consists in the capability of extracting modal parameters from the recorded time signals, without the requirement of excitation measurement. The excitation is supposed to be a stationary Gaussian white noise. The method also allows the estimation of modal parameter uncertainties. On the basis of these uncertainties, a statistically based damage detection scheme is performed and it becomes possible to assess whether changes of modal parameters are caused by, e.g. some damage or simply by estimation inaccuracies. The paper reports first an example of identification and damage detection applied to a simulated system under random excitation. The `Steel-Quake' benchmark proposed in the framework of COST Action F3 `Structural Dynamics' is also analysed. This structure was defined by the Joint Research Centre in Ispra (Italy) to test steel building performance during earthquakes. The proposed method gives an excellent identification of frequencies and mode shapes, while damping ratios are estimated with less accuracy.

  11. AUTOREGRESSIVE MODEL AND POWER SPECTRUM CHARATERISTICS OF CURRENT SIGNAL IN HIGH FREQUENCY GROUP PULSE MICRO ELECTROCHEMICAL MACHINING

    Institute of Scientific and Technical Information of China (English)

    TANG Xinglun; ZHANG Zhijing; ZHOU Zhaoying; YANG Xiaodong

    2006-01-01

    The identification of the inter-electrode gap size in the high frequency group pulse micro-electrochemical machining (HGPECM) is mainly discussed. The auto-regressive(AR) model of group pulse current flowing across the cathode and the anode are created under different situations with different processing parameters and inter-electrode gap size. The AR model based on the current signals indicates that the order of the AR model is obviously different relating to the different processing conditions and the inter-electrode gap size; Moreover, it is different about the stability of the dynamic system, i.e. the white noise response of the Green's function of the dynamic system is diverse. In addition, power spectrum method is used in the analysis of the dynamic time series about the current signals with different inter-electrode gap size, the results show that there exists a strongest power spectrum peak, characteristic power spectrum(CPS), to the current signals related to the different inter-electrode gap size in the range of 0~5 kHz. Therefore, the CPS of current signals can implement the identification of the inter-electrode gap.

  12. Toward the quantification of a conceptual framework for movement ecology using circular statistical modeling.

    Directory of Open Access Journals (Sweden)

    Ichiro Ken Shimatani

    Full Text Available To analyze an animal's movement trajectory, a basic model is required that satisfies the following conditions: the model must have an ecological basis and the parameters used in the model must have ecological interpretations, a broad range of movement patterns can be explained by that model, and equations and probability distributions in the model should be mathematically tractable. Random walk models used in previous studies do not necessarily satisfy these requirements, partly because movement trajectories are often more oriented or tortuous than expected from the models. By improving the modeling for turning angles, this study aims to propose a basic movement model. On the basis of the recently developed circular auto-regressive model, we introduced a new movement model and extended its applicability to capture the asymmetric effects of external factors such as wind. The model was applied to GPS trajectories of a seabird (Calonectris leucomelas to demonstrate its applicability to various movement patterns and to explain how the model parameters are ecologically interpreted under a general conceptual framework for movement ecology. Although it is based on a simple extension of a generalized linear model to circular variables, the proposed model enables us to evaluate the effects of external factors on movement separately from the animal's internal state. For example, maximum likelihood estimates and model selection suggested that in one homing flight section, the seabird intended to fly toward the island, but misjudged its navigation and was driven off-course by strong winds, while in the subsequent flight section, the seabird reset the focal direction, navigated the flight under strong wind conditions, and succeeded in approaching the island.

  13. Short-term wind speed forecasting model based on ARMA-LSSVM and wavelet transform%基于小波变换的ARMA-LSSVM短期风速预测

    Institute of Scientific and Technical Information of China (English)

    赵辉; 李斌; 李彪; 岳有军

    2012-01-01

    对风电场风速的准确预测,可以有效减轻并网后风电对电网的影响,提高风电市场竞争力.提出将时间序列自回归滑动平均模型(Auto Regressive Moving Average,ARMA)与最小二乘支持向量机模型(Least Square Support Vector Machine,LS-SVM)相结合的混合模型短期风速预测方法.采用小波变换(Wavelet Transform,WT)方法将历史风速序列分解成具有不同频率特征的序列.根据分解后各分量的特点,对于低频趋势分量选取LS-SVM方法进行预测,而高频波动分量则选取ARMA模型进行预测,采用小波重构得到最终预测结果.仿真实例表明,不同的预测方法整体的预测精度不同,而混合模型预测的均方根误差最低为11.5%,与单一预测方法相比,混合模型提高了预测精度.%A wind speed forecasting with high accuracy can effectively reduce or avoid the adverse effect of wind farm on power grids, meanwhile it can enhance the competitive ability of wind power in electricity market. A short-term wind speed forecasting method based on auto-regressive moving average (ARMA) model and least square support vector machine (LS-SWM) model was proposed. By using wavelet transform method, the historical load data was decomposed into series with different frequency characteristics. The low frequency components were predicted by LS-SVM model, while the high frequency components were predicted by ARMA model. The final forecasting results were obtained with wavelet reconstruction. Research results show that the prediction accuracy is different from each method. The mean square error of the proposed hybrid forecast model is 11.5%, better than the results by single forecasting methods.

  14. 一种适应户外光照变化的背景建模及目标检测方法%Background Modeling Adaptive to Outdoor Illumination Variation and Foreground Detection Approach

    Institute of Scientific and Technical Information of China (English)

    赵旭东; 刘鹏; 唐降龙; 刘家锋

    2011-01-01

    针对户外视频监控存在光照变化这一问题,提出一个用于准确完成目标检测的实时背景建模框架.考虑到目标检测的准确性要求,建立基于帧间像素亮度差统计直方图的像素亮度扰动阈值.在此基础上,针对背景建模的实时性要求,提出一种基于自回归背景模型的参数快速更新方法.鉴于不同光照变化的适应性要求,定义对光照变化不敏感的背景纹理模型.上述模型统称为自回归-纹理(Auto regression and texture,ART)模型,该模型适应于户外光照变化.基于该模型构建像素亮度和纹理置信区间用于目标检测.实验结果表明,该框架能适应和实时跟踪户外背景的光照变化,并对目标进行准确检测.%Considering the appearance of illumination variation in outdoor video surveillance, a real-time background modeling framework, which is also composed of accurate foreground detection, is established. In view of the accuracy of foreground detection, a threshold based on the histogram of pixel's intensity difference between neighboring frames is proposed. On account of the real-time background modeling, a fast estimation approach on parameters of autoregressive model is presented. Considering the adaptability to variable illumination, a texture background model insensitive to outdoor illumination variation is designed. Thus, a uniform model named auto regression and texture (ART) is obtained. According to the established confidence intervals with perturbation of pixel's intensity and its local texture, foreground in scenes with different illumination variations is successfully detected. The experimental results indicate that the framework is adaptive to and can exactly track outdoor illumination variation in real time. Moreover, foreground detection is successfully accomplished.

  15. A Mixed Logical Dynamical-Model Predictive Control (MLD-MPC Energy Management Control Strategy for Plug-in Hybrid Electric Vehicles (PHEVs

    Directory of Open Access Journals (Sweden)

    Jing Lian

    2017-01-01

    Full Text Available Plug-in hybrid electric vehicles (PHEVs can be considered as a hybrid system (HS which includes the continuous state variable, discrete event, and operation constraint. Thus, a model predictive control (MPC strategy for PHEVs based on the mixed logical dynamical (MLD model and short-term vehicle speed prediction is proposed in this paper. Firstly, the mathematical model of the controlled PHEV is set-up to evaluate the energy consumption using the linearized models of core power components. Then, based on the recognition of driving intention and the past vehicle speed data, a nonlinear auto-regressive (NAR neural network structure is designed to predict the vehicle speed for known driving profiles of city buses and the predicted vehicle speed is used to calculate the total required torque. Next, a MLD model is established with appropriate constraints for six possible driving modes. By solving the objective function with the Mixed Integer Linear Programming (MILP algorithm, the optimal motor torque and the corresponding driving mode sequence within the speed prediction horizon can be obtained. Finally, the proposed energy control strategy shows substantial improvement in fuel economy in the simulation results.

  16. Breast tissue characterization using FARMA modeling of ultrasonic RF echo.

    Science.gov (United States)

    Alacam, Burak; Yazici, Birsen; Bilgutay, Nihat; Forsberg, Flemming; Piccoli, Catherine

    2004-10-01

    A number of empirical and analytical studies demonstrated that the ultrasound RF echo reflected from tissue exhibits 1/f characteristics. In this paper, we propose to model 1/f characteristics of the ultrasonic RF echo by a novel parsimonious model, namely the fractional differencing auto regressive moving average (FARMA) process, and evaluated diagnostic value of model parameters for breast cancer malignancy differentiation. FARMA model captures the fractal and long term correlated nature of the backscattered speckle texture and facilitates robust efficient estimation of fractal parameters. In our study, in addition to the computer generated FARMA model parameters, we included patient age and radiologist's prebiopsy level of suspicion (LOS) as potential indicators of malignant and benign masses. We evaluated the performance of the proposed set of features using various classifiers and training methods using 120 in vivo breast images. Our study shows that the area under the receiver operating characteristics (ROC) curve of FARMA model parameters alone is superior to the area under the ROC curve of the radiologist's prebiopsy LOS. The area under the ROC curve of the three sets of features yields a value of 0.87, with a confidence interval of [0.85, 0.89], at a significance level of 0.05. Our results suggest that the proposed method of ultrasound RF echo model leads to parameters that can differentiate breast tumors with a relatively high precision. This set of RF echo features can be incorporated into a comprehensive computer-aided diagnostic system to aid physicians in breast cancer diagnosis.

  17. Modeling and energy management control design for a fuel cell hybrid passenger bus

    Science.gov (United States)

    Simmons, Kyle; Guezennec, Yann; Onori, Simona

    2014-01-01

    This paper presents the modeling and supervisory energy management design of a hybrid fuel cell/battery-powered passenger bus. With growing concerns about petroleum usage and greenhouse gas emissions in the transportation sector, finding alternative methods for vehicle propulsion is necessary. Proton Exchange Membrane (PEM) fuel cell systems are viable possibilities for energy converters due to their high efficiencies and zero emissions. It has been shown that the benefits of PEM fuel cell systems can be greatly improved through hybridization. In this work, the challenge of developing an on-board energy management strategy with near-optimal performance is addressed by a two-step process. First, an optimal control based on Pontryagin's Minimum Principle (PMP) is implemented to find the global optimal solution which minimizes fuel consumption, for different drive cycles, with and without grade. The optimal solutions are analyzed in order to aid in development of a practical controller suitable for on-board implementation, in the form of an Auto-Regressive Moving Average (ARMA) regulator. Simulation results show that the ARMA controller is capable of achieving fuel economy within 3% of the PMP controller while being able to limit the transient demand on the fuel cell system.

  18. Urban Growth Modeling Using Anfis Algorithm: a Case Study for Sanandaj City, Iran

    Science.gov (United States)

    Mohammady, S.; Delavar, M. R.; Pijanowski, B. C.

    2013-10-01

    not easy to obtain the optimal structure. Since, in this type of fuzzy logic, neural network has been used, therefore, by using a learning algorithm the parameters have been changed until reach the optimal solution. Adaptive Neuro Fuzzy Inference System (ANFIS) computing due to ability to understand nonlinear structures is a popular framework for solving complex problems. Fusion of ANN and FIS has attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. In this research, an ANFIS method has been developed for modeling land use change and interpreting the relationship between the drivers of urbanization. Our study area is the city of Sanandaj located in the west of Iran. Landsat images acquired in 2000 and 2006 have been used for model development and calibration. The parameters used in this study include distance to major roads, distance to residential regions, elevation, number of urban pixels in a 3 by 3 neighborhood and distance to green space. Percent Correct Match (PCM) and Figure of Merit were used to assess model goodness of fit were 93.77% and 64.30%, respectively.

  19. URBAN GROWTH MODELING USING ANFIS ALGORITHM: A CASE STUDY FOR SANANDAJ CITY, IRAN

    Directory of Open Access Journals (Sweden)

    S. Mohammady

    2013-10-01

    , however, it is not easy to obtain the optimal structure. Since, in this type of fuzzy logic, neural network has been used, therefore, by using a learning algorithm the parameters have been changed until reach the optimal solution. Adaptive Neuro Fuzzy Inference System (ANFIS computing due to ability to understand nonlinear structures is a popular framework for solving complex problems. Fusion of ANN and FIS has attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. In this research, an ANFIS method has been developed for modeling land use change and interpreting the relationship between the drivers of urbanization. Our study area is the city of Sanandaj located in the west of Iran. Landsat images acquired in 2000 and 2006 have been used for model development and calibration. The parameters used in this study include distance to major roads, distance to residential regions, elevation, number of urban pixels in a 3 by 3 neighborhood and distance to green space. Percent Correct Match (PCM and Figure of Merit were used to assess model goodness of fit were 93.77% and 64.30%, respectively.

  20. Dynamic fuel cell stack model for real-time simulation based on system identification

    Science.gov (United States)

    Meiler, M.; Schmid, O.; Schudy, M.; Hofer, E. P.

    The authors have been developing an empirical mathematical model to predict the dynamic behaviour of a polymer electrolyte membrane fuel cell (PEMFC) stack. Today there is a great number of models, describing steady-state behaviour of fuel cells by estimating the equilibrium voltage for a certain set of operating parameters, but models capable of predicting the transient process between two steady-state points are rare. However, in automotive applications round about 80% of operating situations are dynamic. To improve the reliability of fuel cell systems by model-based control for real-time simulation dynamic fuel cell stack model is needed. Physical motivated models, described by differential equations, usually are complex and need a lot of computing time. To meet the real-time capability the focus is set on empirical models. Fuel cells are highly nonlinear systems, so often used auto-regressive (AR), output-error (OE) or Box-Jenkins (BJ) models do not accomplish satisfying accuracy. Best results are achieved by splitting the behaviour into a nonlinear static and a linear dynamic subsystem, a so-called Uryson-Model. For system identification and model validation load steps with different amplitudes are applied to the fuel cell stack at various operation points and the voltage response is recorded. The presented model is implemented in MATLAB environment and has a computing time of less than 1 ms per step on a standard desktop computer with a 2.8 MHz CPU and 504 MB RAM. Lab tests are carried out at DaimlerChrysler R&D Centre with DaimlerChrysler PEMFC hardware and a good agreement is found between model simulations and lab tests.

  1. Dynamic fuel cell stack model for real-time simulation based on system identification

    Energy Technology Data Exchange (ETDEWEB)

    Meiler, M.; Schmid, O.; Schudy, M. [Department of MEA and Stack Technology, DaimlerChrysler AG, Neue Str. 95, D-73230 Kirchheim/Teck (Germany); Hofer, E.P. [Department of Measurement, Control and Microtechnology, University of Ulm, Albert-Einstein-Allee 41, D-89081 Ulm (Germany)

    2008-02-01

    The authors have been developing an empirical mathematical model to predict the dynamic behaviour of a polymer electrolyte membrane fuel cell (PEMFC) stack. Today there is a great number of models, describing steady-state behaviour of fuel cells by estimating the equilibrium voltage for a certain set of operating parameters, but models capable of predicting the transient process between two steady-state points are rare. However, in automotive applications round about 80% of operating situations are dynamic. To improve the reliability of fuel cell systems by model-based control for real-time simulation dynamic fuel cell stack model is needed. Physical motivated models, described by differential equations, usually are complex and need a lot of computing time. To meet the real-time capability the focus is set on empirical models. Fuel cells are highly nonlinear systems, so often used auto-regressive (AR), output-error (OE) or Box-Jenkins (BJ) models do not accomplish satisfying accuracy. Best results are achieved by splitting the behaviour into a nonlinear static and a linear dynamic subsystem, a so-called Uryson-Model. For system identification and model validation load steps with different amplitudes are applied to the fuel cell stack at various operation points and the voltage response is recorded. The presented model is implemented in MATLAB environment and has a computing time of less than 1 ms per step on a standard desktop computer with a 2.8 MHz CPU and 504 MB RAM. Lab tests are carried out at DaimlerChrysler R and D Centre with DaimlerChrysler PEMFC hardware and a good agreement is found between model simulations and lab tests. (author)

  2. Model-based Pedestrian Trajectory Prediction using Environmental Sensor for Mobile Robots Navigation

    Directory of Open Access Journals (Sweden)

    Haruka Tonoki

    2017-02-01

    Full Text Available Safety is the most important to the mobile robots that coexist with human. There are many studies that investigate obstacle detection and collision avoidance by predicting obstacles’ trajectories several seconds into the future using mounted sensors such as cameras and laser range finder (LRF for the safe behavior control of robots. In environments such as crossing roads where blind areas occur because of visual barriers like walls, obstacle detection might be delayed and collisions might be difficult to avoid. Using environmental sensors to detect obstacles is effective in such environments. When crossing roads, there are several passages pedestrian might move and it is difficult to depict going each passage in the same movement model. Therefore, we hypothesize that a more effective way to predict pedestrian movement is by predicting passages pedestrian might move and estimating the trajectories to the passages. We acquire pedestrian trajectory data using an environmental LRF with an extended Kalman filter (EKF and construct pedestrian movement models using vector auto regressive (VAR models, which pedestrian state is consisting of the position, speed and direction. Then, we test the validity of the constructed pedestrian movement models using experimental data. We narrow down the selection of a pedestrian movement model by comparing the prediction error for each path between the estimated pedestrian state using an EKF, and the predicted state using each movement model. We predict the trajectory using the selected movement model. Finally, we confirm that an appropriate path model that a pedestrian can actually move through is selected before the crossing area and that only the appropriate model is selected near the crossing area.

  3. Estimation of rotor effective wind speeds using autoregressive models on Lidar data

    Science.gov (United States)

    Giyanani, A.; Bierbooms, W. A. A. M.; van Bussel, G. J. W.

    2016-09-01

    Lidars have become increasingly useful for providing accurate wind speed measurements in front of the wind turbine. The wind field measured at distant meteorological masts changes its structure or was too distorted before it reaches the turbine. Thus, one cannot simply apply Taylor's frozen turbulence for representing this distant flow field at the rotor. Wind turbine controllers can optimize the energy output and reduce the loads significantly, if the wind speed estimates were known in advance with high accuracy and low uncertainty. The current method to derive wind speed estimations from aerodynamic torque, pitch angle and tip speed ratio after the wind field flows past the turbine and have their limitations, e.g. in predicting gusts. Therefore, an estimation model coupled with the measuring capability of nacelle based Lidars was necessary for detecting extreme events and for estimating accurate wind speeds at the rotor disc. Nacelle-mounted Lidars measure the oncoming wind field from utpo 400m(5D) in front of the turbine and appropriate models could be used for deriving the rotor effective wind speed from these measurements. This article proposes an auto-regressive model combined with a method to include the blockage factor in order to estimate the wind speeds accurately using Lidar measurements. An Armax model was used to determine the transfer function that models the physical evolution of wind towards the wind turbine, incorporating the effect of surface roughness, wind shear and wind variability at the site. The model could incorporate local as well as global effects and was able to predict the rotor effective wind speeds with adequate accuracy for wind turbine control actions. A high correlation of 0.86 was achieved as the Armax modelled signal was compared to a reference signal. The model could also be extended to estimate the damage potential during high wind speeds, gusts or abrupt change in wind directions, allowing the controller to act appropriately

  4. Acoustic monitoring of sodium boiling in a liquid metal fast breeder reactor from autoregressive models

    Energy Technology Data Exchange (ETDEWEB)

    Geraldo, Issa Cherif [Laboratoire d’Automatique, Génie Informatique et Signal (LAGIS UMR CNRS 8219), Université Lille 1, Sciences et technologies, Avenue Paul Langevin, BP 48, 59651 Villeneuve d’Ascq CEDEX (France); Bose, Tanmoy [Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal (India); Pekpe, Komi Midzodzi, E-mail: midzodzi.pekpe@univ-lille1.fr [Laboratoire d’Automatique, Génie Informatique et Signal (LAGIS UMR CNRS 8219), Université Lille 1, Sciences et technologies, Avenue Paul Langevin, BP 48, 59651 Villeneuve d’Ascq CEDEX (France); Cassar, Jean-Philippe [Laboratoire d’Automatique, Génie Informatique et Signal (LAGIS UMR CNRS 8219), Université Lille 1, Sciences et technologies, Avenue Paul Langevin, BP 48, 59651 Villeneuve d’Ascq CEDEX (France); Mohanty, A.R. [Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal (India); Paumel, Kévin [CEA, DEN, Nuclear Technology Department, F-13108 Saint-Paul-lez-Durance (France)

    2014-10-15

    Highlights: • The work deals with sodium boiling detection in a liquid metal fast breeder reactor. • The authors choose to use acoustic data instead of thermal data. • The method is designed to not to be disturbed by the environment noises. • A real time boiling detection methods are proposed in the paper. - Abstract: This paper deals with acoustic monitoring of sodium boiling in a liquid metal fast breeder reactor (LMFBR) based on auto regressive (AR) models which have low computational complexities. Some authors have used AR models for sodium boiling or sodium–water reaction detection. These works are based on the characterization of the difference between fault free condition and current functioning of the system. However, even in absence of faults, it is possible to observe a change in the AR models due to the change of operating mode of the LMFBR. This sets up the delicate problem of how to distinguish a change in operating mode in absence of faults and a change due to presence of faults. In this paper we propose a new approach for boiling detection based on the estimation of AR models on sliding windows. Afterwards, classification of the models into boiling or non-boiling models is made by comparing their coefficients by two statistical methods, multiple linear regression (LR) and support vectors machines (SVM). The proposed approach takes into account operating mode information in order to avoid false alarms. Experimental data include non-boiling background noise data collected from Phenix power plant (France) and provided by the CEA (Commissariat à l’Energie Atomique et aux énergies alternatives, France) and boiling condition data generated in laboratory. High boiling detection rates as well as low false alarms rates obtained on these experimental data show that the proposed method is efficient for boiling detection. Most importantly, it shows that the boiling phenomenon introduces a disturbance into the AR models that can be clearly detected.

  5. Aplicación del modelo neurodifuso ANFIS vs redes neuronales, al problema predictivo de caudales medios mensuales del río Bogotá en Villapinzón

    Directory of Open Access Journals (Sweden)

    Edagar Gómez Vargas

    2010-12-01

    Full Text Available This paper shows the results in the exploration of the benefits of the implementation of neuro-fuzzy ANFIS model and neural networks for the prediction of monthly mean flows in the basin of Bogota River in Villapinzón. The ANFIS model is developed, implemented and the performance of six models is evaluated bychanging entries number, number and type of fuzzy sets (membership functions, which are the basic parameters of the ANFIS model. The results are compared with those obtained with multilayer perceptron neural networks.

  6. Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?

    Directory of Open Access Journals (Sweden)

    Simionescu Mihaela

    2015-06-01

    Full Text Available This paper brings to light an economic problem that frequently appears in practice: For the same variable, more alternative forecasts are proposed, yet the decision-making process requires the use of a single prediction. Therefore, a forecast assessment is necessary to select the best prediction. The aim of this research is to propose some strategies for improving the unemployment rate forecast in Romania by conducting a comparative accuracy analysis of unemployment rate forecasts based on two quantitative methods: Kalman filter and vector-auto-regressive (VAR models. The first method considers the evolution of unemployment components, while the VAR model takes into account the interdependencies between the unemployment rate and the inflation rate. According to the Granger causality test, the inflation rate in the first difference is a cause of the unemployment rate in the first difference, these data sets being stationary. For the unemployment rate forecasts for 2010-2012 in Romania, the VAR models (in all variants of VAR simulations determined more accurate predictions than Kalman filter based on two state space models for all accuracy measures. According to mean absolute scaled error, the dynamic-stochastic simulations used in predicting unemployment based on the VAR model are the most accurate. Another strategy for improving the initial forecasts based on the Kalman filter used the adjusted unemployment data transformed by the application of the Hodrick-Prescott filter. However, the use of VAR models rather than different variants of the Kalman filter methods remains the best strategy in improving the quality of the unemployment rate forecast in Romania. The explanation of these results is related to the fact that the interaction of unemployment with inflation provides useful information for predictions of the evolution of unemployment related to its components (i.e., natural unemployment and cyclical component.

  7. System identification and automatic tuning of the controller in hydro power plants; Systemidentifikation und Reglerselbsteinstellung in Wasserkraftanlagen

    Energy Technology Data Exchange (ETDEWEB)

    Anz, R.

    2002-07-01

    In this work a method is presented to generate dynamic nonlinear models for speed and power controlled hydro Power plants. The models are identified automatically with measured data during operation. The models can be used for optimisation of the parameters of the controller. In this approach local linear neuro-fuzzy models are used. They seem very suitable for modelling nonlinear static and dynamic Systems. For a given set of measured data the structure and the parameters of the model are generated with the LOLIMOT-algorithm which is well known from literature. Several modifications of this algorithm are investigated during application on hydro power stations. Unfortunately not sufficient measured data from real power plants were available therefore theoretical models based on physical law and equations had to be used instead. The parameters for speed and power control are optimises using a global optimisation method. Other optimisation and design methods can be used and are discussed. The controllers which are optimised with the experimentally generated local linear neuro-fuzzy model are tested with the theoretical model. A clear improvement of the controller can be confirmed. (orig.) [German] In der vorliegenden Arbeit wird ein Verfahren vorgestellt, mit dem dynamische Modelle von drehzahl- und leistungsgeregelten Wasserkraftanlagen aus gemessenen Betriebsdaten automatisch bestimmt werden koennen. Diese Modelle koennen fuer den Entwurf oder zur Optimierung von Reglerparametern herangezogen werden. Bei dem dynamischen Modell handelt es sich um ein lokal lineares Neuro-Fuzzy Netz. Dieser Ansatz ist geeignet, nichtlineare statische und dynamische Systeme abzubilden. Fuer einen gegebenen Satz gemessener Daten erfolgt die Modellerstellung weitgehend automatisch mit dem aus der Literatur bekannten LOLIMOT-Algorithmus. Verschiedene Varianten und Abaenderungen des Verfahrens werden am Beispiel von Wasserkraftanlagen in dieser Arbeit untersucht. Leider standen fuer die

  8. 基于自适应模糊神经网络的交通流状态预测%Prediction of Traffic Flow Pattern Based on Adaptive Neuro-fuzzy Inference System

    Institute of Scientific and Technical Information of China (English)

    王辉

    2007-01-01

    研究交通流状态的分类、识别与预测,建立了基于模糊聚类及模式识别的交通流状态自适应模糊神经推理系统.对大量交通流历史特征数据采用模糊聚类法进行状态分类并进行模式识别,得到系统的原始输入输出数据集.建立交通流状态预测的自适应模糊神经系统,以交通流特征数据及其识别结果作为训练数据集进行系统参数及模糊规则的训练与确定,直到误差在控制范围内,并进行系统检测和复核.仿真及其检测和复核结果表明系统预测的准确率在 95%以上.

  9. Traffic Flow Prediction Based on Adaptive Neuro-fuzzy Inference Systems%基于自适应模糊神经推理网络的交通流量预测研究

    Institute of Scientific and Technical Information of China (English)

    侯明善; 兰云

    2006-01-01

    交通流量预测的本质是对具有非周期性、非线性和随机性的交通流量数据序列根据当前和历史数据特征对未来流量态势做出合理的判断.基于模糊神经推理网络的非线性拟合能力和推理机制,研究了自适应模糊神经推理网络ANFIS在交通流量预测中的应用.设计了3种形式的一阶模糊推理网络,对采样周期分别为30 s和2 min的非周期性交通流量进行了预测计算,与具有不同隐层单元的BP神经网络预测结果进行了比较.结果表明自适应模糊神经网络计算简单,在交通流量趋势预测方面优势明显.

  10. 基于自适应神经模糊网络的果蔬抓取力控制%Griping Force Control Using Adaptive Neuro-fuzzy Inference Systems

    Institute of Scientific and Technical Information of China (English)

    周俊; 杨肖蓉; 朱树平

    2014-01-01

    运用自适应神经模糊推理系统设计了农业机器人果蔬抓取力智能控制器.以当前抓取力和滑觉传感器信号的小波变换细节系数作为控制器的输入,末端执行器两指闭合距离作为控制器的输出.基于减法聚类建立模糊推理模型,通过调整聚类半径来优选模糊规则数.给出了训练样本数据集采集方法,并应用梯度下降与最小二乘混合训练算法辨识了控制器的前件参数和结论参数.对所设计的控制器进行了实验验证,结果表明该控制器能够适应果蔬质量、表面摩擦特性等方面的差异.抓取力超调量得到了限制,最大值小于0.8N,可以避免给抓取对象造成机械损伤.

  11. 神经模糊逆模/PID复合控制在CSTR中的应用%Composite Control Combining Neuro-Fuzzy Inverse Control with PID Control and Its Application in a Continuous Stirred Tank Reactor

    Institute of Scientific and Technical Information of China (English)

    刘士荣; 俞金寿

    2001-01-01

    研究了基于广义基函数神经模糊模型的逆系统实现及其直接逆模控制,并提出将直接逆模控制与PD反馈控制相结合的复合控制策略.该控制策略已应用于CSTR的反应浓度控制.仿真结果表明,神经模糊逆模佃D复合控制能克服因辨识逆模型不精确引起的缺陷,并具有良好控制性能.

  12. A New Application of an ANFIS for the Shape Optimal Design of Electromagnetic Devices

    Directory of Open Access Journals (Sweden)

    N. Mohdeb

    2014-09-01

    Full Text Available This paper presents a new model based on simulated annealing algorithm (ASA and adaptive neuro-fuzzy inference system (ANFIS for shape optimization and its applications to electromagnetic devices. The proposed model uses ANFIS system to evaluate the electromagnetic performance of the device. Both the ANFIS and ASA method are applied to the design/optimization of the electromagnetic actuator. The results of the proposed approach are compared with other techniques such as: method of moving asymptotes, penalty method, augmented lagrangian genetic algorithm and simulated annealing method (SA. Among the algorithms, the proposed ANFIS-ASA approach significantly outperforms the other methods.

  13. Prediction of the thickness of the compensator filter in radiation therapy using computational intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Dehlaghi, Vahab; Taghipour, Mostafa; Haghparast, Abbas [Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah (Iran, Islamic Republic of); Roshani, Gholam Hossein [School of Energy, Kermanshah University of Technology, Kermanshah (Iran, Islamic Republic of); Rezaei, Abbas [Department of Electrical Engineering, Kermanshah University of Technology, Kermanshah (Iran, Islamic Republic of); Shayesteh, Sajjad Pashootan [Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah (Iran, Islamic Republic of); Adineh-Vand, Ayoub [Department of Computer Engineering, Islamic Azad University, Kermanshah (Iran, Islamic Republic of); Department of Electrical Engineering, Razi University, Kermanshah (Iran, Islamic Republic of); Karimi, Gholam Reza, E-mail: ghkarimi@razi.ac.ir [Department of Electrical Engineering, Razi University, Kermanshah (Iran, Islamic Republic of)

    2015-04-01

    In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) are investigated to predict the thickness of the compensator filter in radiation therapy. In the proposed models, the input parameters are field size (S), off-axis distance, and relative dose (D/D{sub 0}), and the output is the thickness of the compensator. The obtained results show that the proposed ANN and ANFIS models are useful, reliable, and cheap tools to predict the thickness of the compensator filter in intensity-modulated radiation therapy.

  14. 模糊自适应控制

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    [ 篇名 ] A self tuning predictive controller based on instantaneous linearization using neural networks,[篇名 ] Discrete-time neuro-fuzzy adaptive control based on dynamic inversion for robotic manipulators,[ 篇名 ] Fuzzy adaptive output tracking control of a class of composite systems,[ 篇名 ] Model reference fuzzy adaptive control of dissolved oxygen concentration,[ 篇名 ] Model-reference fuzzy adaptive control as a framework for nonlinear system control,[篇名 ] Multivariable fuzzy adaptive control of nonlinear systems。

  15. Wind turbine condition monitoring based on SCADA data using normal behavior models

    DEFF Research Database (Denmark)

    Schlechtingen, Meik; Santos, Ilmar

    2014-01-01

    This paper is part two of a two part series. The originality of part one was the proposal of a novelty approach for wind turbine supervisory control and data acquisition (SCADA) data mining for condition monitoring purposes. The novelty concerned the usage of adaptive neuro-fuzzy interference......) proposed the prediction errors provide information about the condition of the monitored components.Part two presents application examples illustrating the efficiency of the proposed method. The work is based on continuously measured wind turbine SCADA data from 18 modern type pitch regulated wind turbines...... of the 2 MW class covering a period of 35 months. Several real life faults and issues in this data are analyzed and evaluated by the condition monitoring system (CMS) and the results presented. It is shown that SCADA data contain crucial information for wind turbine operators worth extracting. Using full...

  16. Sequential Adaptive Fuzzy Inference System Based Intelligent Control of Robot Manipulators

    Directory of Open Access Journals (Sweden)

    Sahraoui Mustapha

    2014-10-01

    Full Text Available The present paper is dedicated to the presentation and implementation of an optimized technique allowing an on-line estimation of a robot manipulator parameters to use them in a computed torque control. Indeed the proposed control law needs the exact robot model to give good performances. The complexity of the robot manipulator and its strong non-linearity makes it hard to know its parameters. Therefore, we propose in this paper to use neuro-fuzzy networks Sequential Adaptive Fuzzy Inference System (SAFIS to estimate the parameters of the controlled robot manipulator.

  17. Correlation-based analysis and generation of multiple spike trains using hawkes models with an exogenous input.

    Science.gov (United States)

    Krumin, Michael; Reutsky, Inna; Shoham, Shy

    2010-01-01

    The correlation structure of neural activity is believed to play a major role in the encoding and possibly the decoding of information in neural populations. Recently, several methods were developed for exactly controlling the correlation structure of multi-channel synthetic spike trains (Brette, 2009; Krumin and Shoham, 2009; Macke et al., 2009; Gutnisky and Josic, 2010; Tchumatchenko et al., 2010) and, in a related work, correlation-based analysis of spike trains was used for blind identification of single-neuron models (Krumin et al., 2010), for identifying compact auto-regressive models for multi-channel spike trains, and for facilitating their causal network analysis (Krumin and Shoham, 2010). However, the diversity of correlation structures that can be explained by the feed-forward, non-recurrent, generative models used in these studies is limited. Hence, methods based on such models occasionally fail when analyzing correlation structures that are observed in neural activity. Here, we extend this framework by deriving closed-form expressions for the correlation structure of a more powerful multivariate self- and mutually exciting Hawkes model class that is driven by exogenous non-negative inputs. We demonstrate that the resulting Linear-Non-linear-Hawkes (LNH) framework is capable of capturing the dynamics of spike trains with a generally richer and more biologically relevant multi-correlation structure, and can be used to accurately estimate the Hawkes kernels or the correlation structure of external inputs in both simulated and real spike trains (recorded from visually stimulated mouse retinal ganglion cells). We conclude by discussing the method's limitations and the broader significance of strengthening the links between neural spike train analysis and classical system identification.

  18. Investigations on Hybrid Learning in ANFIS

    Directory of Open Access Journals (Sweden)

    C.Loganathan

    2014-10-01

    Full Text Available Neural networks have attractiveness to several researchers due to their great closeness to the structure of the brain, their characteristics not shared by many traditional systems. An Artificial Neural Network (ANN is a network of interconnected artificial processing elements (called neurons that co-operate with one another in order to solve specific issues. ANNs are inspired by the structure and functional aspects of biological nervous systems. Neural networks, which recognize patterns and adopt themselves to cope with changing environments. Fuzzy inference system incorporates human knowledge and performs inferencing and decision making. The integration of these two complementary approaches together with certain derivative free optimization techniques, results in a novel discipline called Neuro Fuzzy. In Neuro fuzzy development a specific approach is called Adaptive Neuro Fuzzy Inference System (ANFIS, which has shown significant results in modeling nonlinear functions. The basic idea behind the paper is to design a system that uses a fuzzy system to represent knowledge in an interpretable manner and have the learning ability derived from a Runge-Kutta learning method (RKLM to adjust its membership functions and parameters in order to enhance the system performance. The problem of finding appropriate membership functions and fuzzy rules is often a tiring process of trial and error. It requires users to understand the data before training, which is usually difficult to achieve when the database is relatively large. To overcome these problems, a hybrid of Back Propagation Neural network (BPN and RKLM can combine the advantages of two systems and avoid their disadvantages.

  19. Artificial Intelligence Techniques for the Estimation of Direct Methanol Fuel Cell Performance

    Science.gov (United States)

    Hasiloglu, Abdulsamet; Aras, Ömür; Bayramoglu, Mahmut

    2016-04-01

    Artificial neural networks and neuro-fuzzy inference systems are well known artificial intelligence techniques used for black-box modelling of complex systems. In this study, Feed-forward artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are used for modelling the performance of direct methanol fuel cell (DMFC). Current density (I), fuel cell temperature (T), methanol concentration (C), liquid flow-rate (q) and air flow-rate (Q) are selected as input variables to predict the cell voltage. Polarization curves are obtained for 35 different operating conditions according to a statistically designed experimental plan. In modelling study, various subsets of input variables and various types of membership function are considered. A feed -forward architecture with one hidden layer is used in ANN modelling. The optimum performance is obtained with the input set (I, T, C, q) using twelve hidden neurons and sigmoidal activation function. On the other hand, first order Sugeno inference system is applied in ANFIS modelling and the optimum performance is obtained with the input set (I, T, C, q) using sixteen fuzzy rules and triangular membership function. The test results show that ANN model estimates the polarization curve of DMFC more accurately than ANFIS model.

  20. Analysis and prediction of rainfall trends over Bangladesh using Mann-Kendall, Spearman's rho tests and ARIMA model

    Science.gov (United States)

    Rahman, Mohammad Atiqur; Yunsheng, Lou; Sultana, Nahid

    2016-09-01

    In this study, 60-year monthly rainfall data of Bangladesh were analysed to detect trends. Modified Mann-Kendall, Spearman's rho tests and Sen's slope estimators were applied to find the long-term annual, dry season and monthly trends. Sequential Mann-Kendall analysis was applied to detect the potential trend turning points. Spatial variations of the trends were examined using inverse distance weighting (IDW) interpolation. AutoRegressive integrated moving average (ARIMA) model was used for the country mean rainfall and for other two stations data which depicted the highest and the lowest trend in the Mann-Kendall and Spearman's rho tests. Results showed that there is no significant trend in annual rainfall pattern except increasing trends for Cox's Bazar, Khulna, Satkhira and decreasing trend for Srimagal areas. For the dry season, only Bogra area represented significant decreasing trend. Long-term monthly trends demonstrated a mixed pattern; both negative and positive changes were found from February to September. Comilla area showed a significant decreasing trend for consecutive 3 months while Rangpur and Khulna stations confirmed the significant rising trends for three different months in month-wise trends analysis. Rangpur station data gave a maximum increasing trend in April whereas a maximum decreasing trend was found in August for Comilla station. ARIMA models predict +3.26, +8.6 and -2.30 mm rainfall per year for the country, Cox's Bazar and Srimangal areas, respectively. However, all the test results and predictions revealed a good agreement among them in the study.

  1. A Model of Alternate Service Based on System Load%一种基于系统负载的轮流服务模型

    Institute of Scientific and Technical Information of China (English)

    王文丰; 赵跃龙

    2009-01-01

    针对现有集群系统在可用性和服务质量等方面存在的不足,文中引入一种动态k叉树结构,在此基础上提出了一种基于系统负载的轮流服务模型.在该模型中,分别采用了一种自回归负载预测模型、基于选举域划分的多机心跳机制以及动态域主节点的选举机制和算法.理论分析和实验结果表明,该模型大大提高了系统的可靠性和可用性,减少了请求处理的平均响应时间,降低了选举开销,能够满足大规模集群服务高可用性和高服务质量的要求.%In order to improve the availability and quality of service (QoS) of existing cluster systems, a model of alternate service based on the system load is proposed by introducing a dynamic k -ray tree architecture. In this model, an auto-regressive load prediction model, a multi-machine heartbeat mechanism based on the election domain partition, and a leader election mechanism and algorithm in the dynamic domain are all employed. Theoretical analyses and experimental results demonstrate that the proposed model greatly improves the reliability and availability of cluster systems and saves the average response time cost and the election overhead. Thus, it well satisfies the requirements for high availability and QoS of large-scale cluster services.

  2. Comparative Study between ARX and ARMAX System Identification

    Directory of Open Access Journals (Sweden)

    Farzin Piltan

    2017-02-01

    Full Text Available System Identification is used to build mathematical models of a dynamic system based on measured data. To design the best controllers for linear or nonlinear systems, mathematical modeling is the main challenge. To solve this challenge conventional and intelligent identification are recommended. System identification is divided into different algorithms. In this research, two important types algorithm are compared to identifying the highly nonlinear systems, namely: AutoRegressive with eXternal model input (ARX and Auto Regressive moving Average with eXternal model input (Armax Theory. These two methods are applied to the highly nonlinear industrial motor.

  3. Adaptive fuzzy control with output feedback for H infinity tracking of SISO nonlinear systems.

    Science.gov (United States)

    Rigatos, Gerasimos G

    2008-08-01

    Observer-based adaptive fuzzy H(infinity) control is proposed to achieve H(infinity) tracking performance for a class of nonlinear systems, which are subject to model uncertainty and external disturbances and in which only a measurement of the output is available. The key ideas in the design of the proposed controller are (i) to transform the nonlinear control problem into a regulation problem through suitable output feedback, (ii) to design a state observer for the estimation of the non-measurable elements of the system's state vector, (iii) to design neuro-fuzzy approximators that receive as inputs the parameters of the reconstructed state vector and give as output an estimation of the system's unknown dynamics, (iv) to use an H(infinity) control term for the compensation of external disturbances and modelling errors, (v) to use Lyapunov stability analysis in order to find the learning law for the neuro-fuzzy approximators, and a supervisory control term for disturbance and modelling error rejection. The control scheme is tested in the cart-pole balancing problem and in a DC-motor model.

  4. Performance measurement of plate fin heat exchanger by exploration: ANN, ANFIS, GA, and SA

    Directory of Open Access Journals (Sweden)

    A.K. Gupta

    2017-01-01

    Full Text Available An experimental work is conducted on counter flow plate fin compact heat exchanger using offset strip fin under different mass flow rates. The training, testing, and validation set of data has been collected by conducting experiments. Next, artificial neural network merged with Genetic Algorithm (GA utilized to measure the performance of plate-fin compact heat exchanger. The main aim of present research is to measure the performance of plate-fin compact heat exchanger and to provide full explanations. An artificial neural network predicted simulated data, which verified with experimental data under 10–20% error. Then, the authors examined two well-known global search techniques, simulated annealing and the genetic algorithm. The proposed genetic algorithm and Simulated Annealing (SA results have been summarized. The parameters are impartially important for good results. With the emergence of a new data-driven modeling technique, Neuro-fuzzy based systems are established in academic and practical applications. The neuro-fuzzy interference system (ANFIS has also been examined to undertake the problem related to plate-fin heat exchanger performance measurement under various parameters. Moreover, Parallel with ANFIS model and Artificial Neural Network (ANN model has been created with emphasizing the accuracy of the different techniques. A wide range of statistical indicators used to assess the performance of the models. Based on the comparison, it was revealed that technical ANFIS improve the accuracy of estimates in the small pool and tropical ANN.

  5. Causality analysis of groundwater dynamics based on a Vector Autoregressive model in the semi-arid basin of Gundal (South India)

    Science.gov (United States)

    Mangiarotti, S.; Sekhar, M.; Berthon, L.; Javeed, Y.; Mazzega, P.

    2012-08-01

    Causal relationships existing between observed levels of groundwater in a semi-arid sub-basin of the Kabini River basin (Karnataka state, India) are investigated in this study. A Vector Auto Regressive model is used for this purpose. Its structure is built on an upstream/downstream interaction network based on observed hydro-physical properties. Exogenous climatic forcing is used as an input based on cumulated rainfall departure. Optimal models are obtained thanks to a trial approach and are used as a proxy of the dynamics to derive causal networks. It appears to be an interesting tool for analysing the causal relationships existing inside the basin. The causal network reveals 3 main regions: the Northeastern part of the Gundal basin is closely coupled to the outlet dynamics. The Northwestern part is mainly controlled by the climatic forcing and only marginally linked to the outlet dynamic. Finally, the upper part of the basin plays as a forcing rather than a coupling with the lower part of the basin allowing for a separate analysis of this local behaviour. The analysis also reveals differential time scales at work inside the basin when comparing upstream oriented with downstream oriented causalities. In the upper part of the basin, time delays are close to 2 months in the upward direction and lower than 1 month in the downward direction. These time scales are likely to be good indicators of the hydraulic response time of the basin which is a parameter usually difficult to estimate practically. This suggests that, at the sub-basin scale, intra-annual time scales would be more relevant scales for analysing or modelling tropical basin dynamics in hard rock (granitic and gneissic) aquifers ubiquitous in south India.

  6. 高精度时变参数模型谱估计及应用%High Precision Time-Varying Parametric Model Spectrum Estimation and Its Application

    Institute of Scientific and Technical Information of China (English)

    邓卫强; 王跃钢; 杨颖涛

    2011-01-01

    To solve the problem of spectral deviation for Time-Varying Auto-Regressive(TVAR) model parametric spectrum estimation, this paper proposes a TVAR parameter estimation method based on Genetic Algorithm(GA) and combined objective function and the method is used to model the flight vibration time series and its spectrum estimation. An initial estimation of the parameters is acquired by the U-C algorithm; through modern spectrum estimation theory and the necessary condition of extreme value for continuous function, a constraint equation of model parameters is derived and a combined adaptability function is constructed; the GA is used to optimize the initial parameter estimation. Application results verifies the efficiency of the method.%现有时变自回归(TVAR)模型参数谱估计容易导致谱峰漂移.针对该问题,提出一种基于组合目标函数和遗传算法的TVAR参数估计方法,并将之应用于飞行器结构响应序列的建模及谱估计.通过U-C算法获得TVAR模型参数的初始估计;依据现代谱估计理论结合连续函数极值存在的必要条件,推导模型参数的频域约束条件并构造组合目标函数;采用遗传算法对模型参数初始估计值进行优化.应用结果证明了该方法的有效性.

  7. Efficient ECG signal analysis using wavelet technique for arrhythmia detection: an ANFIS approach

    Science.gov (United States)

    Khandait, P. D.; Bawane, N. G.; Limaye, S. S.

    2010-02-01

    This paper deals with improved ECG signal analysis using Wavelet Transform Techniques and employing subsequent modified feature extraction for Arrhythmia detection based on Neuro-Fuzzy technique. This improvement is based on suitable choice of features in evaluating and predicting life threatening Ventricular Arrhythmia . Analyzing electrocardiographic signals (ECG) includes not only inspection of P, QRS and T waves, but also the causal relations they have and the temporal sequences they build within long observation periods. Wavelet-transform is used for effective feature extraction and Adaptive Neuro-Fuzzy Inference System (ANFIS) is considered for the classifier model. In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia and CSE databases, developed for validation purposes. Features based on the ECG waveform shape and heart beat intervals are used as inputs to the classifiers. The performance of the ANFIS model is evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. Cross validation is used to measure the classifier performance. A testing classification accuracy of 95.13% is achieved which is a significant improvement.

  8. Monetary policy, inflation and the level of economic activity in Brazil after the Real Plan: stylized facts from SVAR models

    Directory of Open Access Journals (Sweden)

    Brisne Céspedes

    2008-06-01

    Full Text Available This article uncovers some stylized facts about the short run fluctuations of the Brazilian economy after the Real Plan, giving special attention to the identification of the effects of monetary policy shocks. A distinctive feature of this article is the careful attention paid to monetary policy developments after the Real Plan when dividing our sample into two subsamples (1996:07-1998:08 and 1999:03-2004:12. We use directed acyclic graphs to identify the Structural Vector Autoregression (SVAR models. In contrast with most SVAR analysis for Brazil, we found empirical evidence that supports the view that a contractionary monetary policy indeed reduces the price level.Este artigo obtém alguns fatos estilizados sobre as flutuações de curto prazo da economia brasileira após o Plano Real, dando atenção especial à identificação dos efeitos dos choques da política monetária. Dadas as alterações na política monetária, ocorridos após o Plano Real, optamos por dividir a nossa análise em dois subperíodos (1996:07-1998:08 e 1999:032004:12. Os modelos de Auto-Regressão Vetorial Estrutural (SVAR foram identificados através do uso de grafos acíclicos direcionados. Em contraste com a maioria das análises feitas para o Brasil, que adotam modelos SVAR, encontramos evidências de que uma política monetária contracionista reduz o nível geral de preços.

  9. RSPOP: rough set-based pseudo outer-product fuzzy rule identification algorithm.

    Science.gov (United States)

    Ang, Kai Keng; Quek, Chai

    2005-01-01

    System modeling with neuro-fuzzy systems involves two contradictory requirements: interpretability verses accuracy. The pseudo outer-product (POP) rule identification algorithm used in the family of pseudo outer-product-based fuzzy neural networks (POPFNN) suffered from an exponential increase in the number of identified fuzzy rules and computational complexity arising from high-dimensional data. This decreases the interpretability of the POPFNN in linguistic fuzzy modeling. This article proposes a novel rough set-based pseudo outer-product (RSPOP) algorithm that integrates the sound concept of knowledge reduction from rough set theory with the POP algorithm. The proposed algorithm not only performs feature selection through the reduction of attributes but also extends the reduction to rules without redundant attributes. As many possible reducts exist in a given rule set, an objective measure is developed for POPFNN to correctly identify the reducts that improve the inferred consequence. Experimental results are presented using published data sets and real-world application involving highway traffic flow prediction to evaluate the effectiveness of using the proposed algorithm to identify fuzzy rules in the POPFNN using compositional rule of inference and singleton fuzzifier (POPFNN-CRI(S)) architecture. Results showed that the proposed rough set-based pseudo outer-product algorithm reduces computational complexity, improves the interpretability of neuro-fuzzy systems by identifying significantly fewer fuzzy rules, and improves the accuracy of the POPFNN.

  10. Adaptive fuzzy control of underactuated robotic systems with the use of differential flatness theory

    Science.gov (United States)

    Rigatos, Gerasimos G.

    2013-10-01

    An adaptive fuzzy controller is designed for a class of underactuated nonlinear robotic manipulators, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the robotic system into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H∞ tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked in the case of a 2-DOF planar robotic manipulator that has the structure of a closed-chain mechanism.

  11. 基于神经网络模型的结构参数提取新方法%A NOVEL STRUCTURAL PARAMETER EXTRACTION METHODOLOGY WITH NEURAL NETWORK BASED NONPARAMETRIC MODEL

    Institute of Scientific and Technical Information of China (English)

    许斌; 龚安苏; 贺佳; MASRI Sami F

    2011-01-01

    Structural identification has become an increasingly important topic for health monitoring and performance assessment of engineering structures. On one hand, the auto-regressive and moving average (ARMA)model has been widely employed as a typical time-domain modeling method for dynamic systems. On the other hand, with the ability of mapping any complex function and its parallel computation characteristics, neural networks-based identification method has been widely employed as a nonparametric model for civil and mechanical engineering structures even the weights and thresholds in the trained neural network model do not necessarily have clear physical meaning. The equivalence of the two representative time-domain identification methodologies was studied firstly according to the discrete solution of structural dynamic responses. Then, a novel structural parameters extraction methodology was proposed by the use of excitation and dynamic response measurement time series. Finally, the accuracy and efficiency of the proposed approach were validated via a numerical model with three degree-of-freedoms (DOFs), and via a dynamic test on a four-story model structure with impulse excitation. Results show the structural parameters can be extracted from a nonparametric model with neural networks.%参数识别是结构健康监测、性能评估的关键问题之一.作为一种代表性的动力系统时域参数化模型方法,自回归滑动平均(Auto-regressive and moving average,ARMA)模型在机械和土木工程结构的参数识别中得到了广泛应用;另一方面,尽管一般而言神经网络模型的权重和阈值并不需要具备明确的物理意义,但由于神经网络具有描述复杂函数关系的能力,作为一种非参数化模型方法在结构动力系统的建模和控制领域发挥重要作用.该文首先通过结构运动平衡方程的离散时间解,证明了非参数化神经网络模型与ARMA模型在描述线性结构动力系统的响

  12. Comparison of auditory evoked potentials and the A-line ARX Index for monitoring the hypnotic level during sevoflurane and propofol induction

    DEFF Research Database (Denmark)

    Litvan, H; Jensen, E W; Revuelta, M

    2002-01-01

    Extraction of the middle latency auditory evoked potentials (AEP) by an auto regressive model with exogenous input (ARX) enables extraction of the AEP within 1.7 s. In this way, the depth of hypnosis can be monitored at almost real-time. However, the identification and the interpretation of the a......Extraction of the middle latency auditory evoked potentials (AEP) by an auto regressive model with exogenous input (ARX) enables extraction of the AEP within 1.7 s. In this way, the depth of hypnosis can be monitored at almost real-time. However, the identification and the interpretation...

  13. ARIMA model of time series for forecasting epidemic situation of AIDS%时间序列ARIMA模型在艾滋病疫情预测中的应用

    Institute of Scientific and Technical Information of China (English)

    罗静; 杨书; 张强; 王璐

    2012-01-01

    Objective To explore the feasibility of auto regressive integrated moving average(ARIMA)model of time series to predict the incidence of AIDS. Methods The ARIMA model was established basing on the data of AIDS incidences in Chongqing during 1993 -2009. Results The model of ARIMA(1,1,1) X (0 ,1,0)12 exactly fitted the incidence of AIDS. The predicting values of incidence in July to December 2009 were consistent with the actual change trend of incidence. Conclusion The ARIMA model can be used to exactly simulate the change trend of the incidence of AIDS in time series, which can provide reference for prevention and control of AIDS.%目的 探讨应用时间序列求和自回归移动平均(ARIMA)模型预测艾滋病发病率的可行性.方法 利用重庆市疾病控制部门提供的1993~2009年艾滋病发病情况数据建立ARIMA预测模型.结果 ARIMA(1,1,1)×(0,1,0)12很好地拟合了艾滋病发病率,2009年7~12月的预测值符合实际发病率变动趋势.结论 ARIMA模型很好地模拟艾滋病发病率在时间序列上的变动趋势,可以为疫情防控提供借鉴.

  14. Podemos prever a taxa de cambio brasileira? Evidência empírica utilizando inteligência computacional e modelos econométricos Can we forecast Brazilian exchange rates? Empirical evidences using computational intelligence and econometric models

    Directory of Open Access Journals (Sweden)

    Leandro dos Santos Coelho

    2008-12-01

    evidence that these computational intelligence models are able to provide a more accurate forecast given their capacity for capturing nonlinearities and other stylized facts of financial time series. Thus, this paper investigates the hypothesis that the mathematical models of multilayer perception, radial basis function neural networks (NN, and the Takagi-Sugeno (TS fuzzy systems are able to provide a more accurate out-of-sample forecast than the traditional AutoRegressive Moving Average (ARMA and ARMA Generalized AutoRegressive Conditional Heteroskedasticity (ARMA-GARCH models. Using a series of Brazilian exchange rate (R$/US$ returns with 15 minutes, 60 minutes, 120 minutes, daily and weekly basis, the one-step-ahead forecast performance is compared. The results indicate that forecast performance is strongly related to the series' frequency, possibly due to nonlinearities effects. Besides, the forecasting evaluation shows that NN models perform better than the ARMA and ARMA-GARCH ones. In the trade strategy based on forecasts, NN models achieved higher returns when compared to a buy-and-hold strategy and to the other models considered in this study.

  15. Artificial Intelligent Control for a Novel Advanced Microwave Biodiesel Reactor

    Energy Technology Data Exchange (ETDEWEB)

    Wali, W A; Hassan, K H; Cullen, J D; Al-Shamma' a, A I; Shaw, A; Wylie, S R, E-mail: w.wali@2009.ljmu.ac.uk [Built Environment and Sustainable Technologies Institute (BEST), School of the Built Environment, Faculty of Technology and Environment Liverpool John Moores University, Byrom Street, Liverpool L3 3AF (United Kingdom)

    2011-08-17

    Biodiesel, an alternative diesel fuel made from a renewable source, is produced by the transesterification of vegetable oil or fat with methanol or ethanol. In order to control and monitor the progress of this chemical reaction with complex and highly nonlinear dynamics, the controller must be able to overcome the challenges due to the difficulty in obtaining a mathematical model, as there are many uncertain factors and disturbances during the actual operation of biodiesel reactors. Classical controllers show significant difficulties when trying to control the system automatically. In this paper we propose a comparison of artificial intelligent controllers, Fuzzy logic and Adaptive Neuro-Fuzzy Inference System(ANFIS) for real time control of a novel advanced biodiesel microwave reactor for biodiesel production from waste cooking oil. Fuzzy logic can incorporate expert human judgment to define the system variables and their relationships which cannot be defined by mathematical relationships. The Neuro-fuzzy system consists of components of a fuzzy system except that computations at each stage are performed by a layer of hidden neurons and the neural network's learning capability is provided to enhance the system knowledge. The controllers are used to automatically and continuously adjust the applied power supplied to the microwave reactor under different perturbations. A Labview based software tool will be presented that is used for measurement and control of the full system, with real time monitoring.

  16. Artificial Intelligent Control for a Novel Advanced Microwave Biodiesel Reactor

    Science.gov (United States)

    Wali, W. A.; Hassan, K. H.; Cullen, J. D.; Al-Shamma'a, A. I.; Shaw, A.; Wylie, S. R.

    2011-08-01

    Biodiesel, an alternative diesel fuel made from a renewable source, is produced by the transesterification of vegetable oil or fat with methanol or ethanol. In order to control and monitor the progress of this chemical reaction with complex and highly nonlinear dynamics, the controller must be able to overcome the challenges due to the difficulty in obtaining a mathematical model, as there are many uncertain factors and disturbances during the actual operation of biodiesel reactors. Classical controllers show significant difficulties when trying to control the system automatically. In this paper we propose a comparison of artificial intelligent controllers, Fuzzy logic and Adaptive Neuro-Fuzzy Inference System(ANFIS) for real time control of a novel advanced biodiesel microwave reactor for biodiesel production from waste cooking oil. Fuzzy logic can incorporate expert human judgment to define the system variables and their relationships which cannot be defined by mathematical relationships. The Neuro-fuzzy system consists of components of a fuzzy system except that computations at each stage are performed by a layer of hidden neurons and the neural network's learning capability is provided to enhance the system knowledge. The controllers are used to automatically and continuously adjust the applied power supplied to the microwave reactor under different perturbations. A Labview based software tool will be presented that is used for measurement and control of the full system, with real time monitoring.

  17. Genetic based sensorless hybrid intelligent controller for strip loop formation control between inter-stands in hot steel rolling mills.

    Science.gov (United States)

    Thangavel, S; Palanisamy, V; Duraiswamy, K

    2008-04-01

    Safe operating environment is essential for all complex industrial processes. The safety issues in steel rolling mill when the hot strip passes through consecutive mill stands have been considered in this paper. Formation of sag in strip is a common problem in the rolling process. The excessive sag can lead to scrap runs and damage to machinery. Conventional controllers for mill actuation system are based on a rolling model. The factors like rise in temperature, aging, wear and tear are not taken into account while designing a conventional controller. Therefore, the conventional controller cannot yield a requisite controlled output. In this paper, a new Genetic-neuro-fuzzy hybrid controller without tension sensor has been proposed to optimize the quantum of excessive sag and reduce it. The performance of the proposed controller has been compared with the performance of fuzzy logic controller, Neuro-fuzzy controller and conventional controller with the help of data collected from the plant. The simulation results depict that the proposed controller has superior performance than the other controllers.

  18. How does Poisson kriging compare to the popular BYM model for mapping disease risks?

    Directory of Open Access Journals (Sweden)

    Gebreab Samson

    2008-02-01

    Full Text Available Abstract Background Geostatistical techniques are now available to account for spatially varying population sizes and spatial patterns in the mapping of disease rates. At first glance, Poisson kriging represents an attractive alternative to increasingly popular Bayesian spatial models in that: 1 it is easier to implement and less CPU intensive, and 2 it accounts for the size and shape of geographical units, avoiding the limitations of conditional auto-regressive (CAR models commonly used in Bayesian algorithms while allowing for the creation of isopleth risk maps. Both approaches, however, have never been compared in simulation studies, and there is a need to better understand their merits in terms of accuracy and precision of disease risk estimates. Results Besag, York and Mollie's (BYM model and Poisson kriging (point and area-to-area implementations were applied to age-adjusted lung and cervix cancer mortality rates recorded for white females in two contrasted county geographies: 1 state of Indiana that consists of 92 counties of fairly similar size and shape, and 2 four states in the Western US (Arizona, California, Nevada and Utah forming a set of 118 counties that are vastly different geographical units. The spatial support (i.e. point versus area has a much smaller impact on the results than the statistical methodology (i.e. geostatistical versus Bayesian models. Differences between methods are particularly pronounced in the Western US dataset: BYM model yields smoother risk surface and prediction variance that changes mainly as a function of the predicted risk, while the Poisson kriging variance increases in large sparsely populated counties. Simulation studies showed that the geostatistical approach yields smaller prediction errors, more precise and accurate probability intervals, and allows a better discrimination between counties with high and low mortality risks. The benefit of area-to-area Poisson kriging increases as the county

  19. How does Poisson kriging compare to the popular BYM model for mapping disease risks?

    Science.gov (United States)

    Goovaerts, Pierre; Gebreab, Samson

    2008-01-01

    Background Geostatistical techniques are now available to account for spatially varying population sizes and spatial patterns in the mapping of disease rates. At first glance, Poisson kriging represents an attractive alternative to increasingly popular Bayesian spatial models in that: 1) it is easier to implement and less CPU intensive, and 2) it accounts for the size and shape of geographical units, avoiding the limitations of conditional auto-regressive (CAR) models commonly used in Bayesian algorithms while allowing for the creation of isopleth risk maps. Both approaches, however, have never been compared in simulation studies, and there is a need to better understand their merits in terms of accuracy and precision of disease risk estimates. Results Besag, York and Mollie's (BYM) model and Poisson kriging (point and area-to-area implementations) were applied to age-adjusted lung and cervix cancer mortality rates recorded for white females in two contrasted county geographies: 1) state of Indiana that consists of 92 counties of fairly similar size and shape, and 2) four states in the Western US (Arizona, California, Nevada and Utah) forming a set of 118 counties that are vastly different geographical units. The spatial support (i.e. point versus area) has a much smaller impact on the results than the statistical methodology (i.e. geostatistical versus Bayesian models). Differences between methods are particularly pronounced in the Western US dataset: BYM model yields smoother risk surface and prediction variance that changes mainly as a function of the predicted risk, while the Poisson kriging variance increases in large sparsely populated counties. Simulation studies showed that the geostatistical approach yields smaller prediction errors, more precise and accurate probability intervals, and allows a better discrimination between counties with high and low mortality risks. The benefit of area-to-area Poisson kriging increases as the county geography becomes more

  20. 设备故障评估新指标及基于ARMA的预测系统%Innovative metrics for equipment failure evaluation and prediction system based on ARMA model

    Institute of Scientific and Technical Information of China (English)

    李波; 赵洁; 郭晋

    2011-01-01

    设备故障停机时间受生产调度的影响较大,不能真实反映设备的自身性能,且具有很强的随机性和波动性,不适于直接用来进行自回归移动平均(auto-regressive moving average,ARMA)建模.针对此问题,提出一种设备故障评估指标--设备不可用度,将设备故障停机时间转换为设备不可用度,通过异常点替代和数据平稳化等两种数据预处理,建立零均值平稳随机序列进行ARMA建模,并把预测结果转换为设备在一定时间内的故障发生概率.在某半导体芯片封装测试工厂的试验结果表明该方法能以70%的精度预测设备状态,在一个班(12 h)里设备不可用度平均降低2.62%,设备故障停机时间平均减少14.8 min.%The equipment failure down time (or the failure rate) in a certain period of time (such as 12 hours) ,which is greatly influenced by the production planning and scheduling in semiconductor manufacturing factory, can not reflect the true equipment performance. Moreover, the data series of down time is not suitable for being directly used for auto-regressive moving average (ARMA) modeling because it has very strong randomness and undulatory property.An innovative metrics for equipment failure evaluation, named equipment unavailability (EU) , is proposed according to this problem. When building an ARMA model. the equipment failure down time is firstly transformed to EU. Then the data is converted into stationary random sequence by outliers' replacing, and thirdly the trend term of data is removed by using an improved moving average algorithm. So the zero-mean stationary random sequence is available to build the ARMA model. The forecasting result is transformed into equipment failure probability in a certain period of time at last. The process of data pretreatment, modeling, forecasting and result transforming is realized to a software application system by using VS. NET. The application in a chipset assembly and test factory shows