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...
HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.
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.
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...
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 ...
Using Adaptive Neuro-Fuzzy Inference System in Alert Management of Intrusion Detection Systems
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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.
Exploration of the Adaptive Neuro - Fuzzy Inference System Architecture and its Applications
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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.
UAV Controller Based on Adaptive Neuro-Fuzzy Inference System and PID
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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.
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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.
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....
PREDIKSI CUACA MENGGUNAKAN METODE CASE BASED REASONING DAN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM
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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.
APPLICATION OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM IN INTEREST RATES EFFECTS ON STOCK RETURNS
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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.
REPLACEMENT SPARE PART INVENTORY MONITORING USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM
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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
Predicting Packet Transmission Data over IP Networks Using Adaptive Neuro-Fuzzy Inference Systems
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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.
Designing a Battlefield Fire Support System Using Adaptive Neuro-Fuzzy Inference System Based Model
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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.
Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.
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.
Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE
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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.
Prediksi Penjualan Barang Menggunakan Metode Adaptive Neuro-Fuzzy Inference System (ANFIS
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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.
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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.
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.
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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.
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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.
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...
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.
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.
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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.
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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.
Adaptive neuro-fuzzy controller of switched reluctance motor
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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.
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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.
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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.
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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.
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.
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)
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.
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.
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.
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.
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.
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.
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.
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.
Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning
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…
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.
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.
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.
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.
Adaptive Neuro-fuzzy approach in friction identification
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
Adaptive neuro-fuzzy estimation of optimal lens system parameters
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.
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.
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.
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...
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...
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)
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。
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.
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.
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
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.
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.
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.
Development of quantum-based adaptive neuro-fuzzy networks.
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.
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.
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.
Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration
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.
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
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.
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.
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.
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.
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.
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.
Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge.
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.
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.
Training Hybrid Neuro-Fuzzy System to Infer Permeability in Wells on Maracaibo Lake, Venezuela
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.
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.
Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.
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.
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
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.
Adaptive Functional-Based Neuro-Fuzzy-PID Incremental Controller Structure
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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.
Adaptive Neuro-Fuzzy Controller Experimental Design for DC Motor Connected to Unbalanced Load
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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.
Using Hierarchical Adaptive Neuro Fuzzy Systems And Design Two New Edge Detectors In Noisy Images
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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.
Indirect adaptive control of nonlinear systems based on bilinear neuro-fuzzy approximation.
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
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.
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.
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.
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
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.
A novel Neuro-fuzzy classification technique for data mining
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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.
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.
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
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
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.
Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization
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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.
Adaptive Critic Based Neuro-Fuzzy Tracker for Improving Conversion Efficiency in PV Solar Cells
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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.
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.
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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.
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.
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.
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...
Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system
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
MI-ANFIS: A Multiple Instance Adaptive Neuro-Fuzzy Inference System
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
Neuro-fuzzy controller to navigate an unmanned vehicle.
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).
Estimating the crowding level with a neuro-fuzzy classifier
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.
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...
Potential of neuro-fuzzy methodology to estimate noise level of wind turbines
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.
Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach
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
Neuro-Fuzzy Phasing of Segmented Mirrors
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.
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...
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.
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.
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.
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).
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.
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.
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)
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.
Institute of Scientific and Technical Information of China (English)
无
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.
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
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.
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
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.
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.
A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems
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....
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.
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.
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)
Hybrid Neuro-Fuzzy Systems for Software Development Effort Estimation
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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.
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.
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.
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.
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.
Recognition of Handwritten Arabic words using a neuro-fuzzy network
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.
Active Head Motion Compensation of TMS Robotic System Using Neuro-Fuzzy Estimation
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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.
Neuro fuzzy control of the FES assisted freely swinging leg of paraplegic subjects
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
Simulation of neuro-fuzzy model for optimization of combine header setting
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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
A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data
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
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.
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.
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...
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.
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.
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.
Ant colony optimization algorithm and its application to Neuro-Fuzzy controller design
Institute of Scientific and Technical Information of China (English)
无
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.
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.
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.
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)).
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.
Extraction of rules for faulty bearing classification by a Neuro-Fuzzy approach
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.
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
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.
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.
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.
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.
Multimodel inference and adaptive management
Rehme, S.E.; Powell, L.A.; Allen, C.R.
2011-01-01
Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study’s inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.
Efficient neuro-fuzzy system and its Memristor Crossbar-based Hardware Implementation
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.
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.
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
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”.
Characterizing root distribution with adaptive neuro-fuzzy analysis
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...
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.
Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
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...
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.
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.
Adaptive Semisupervised Inference
Azizyan, Martin; Wasserman, Larry
2011-01-01
Semisupervised methods inevitably invoke some assumption that links the marginal distribution of the features to the regression function of the label. Most commonly, the cluster or manifold assumptions are used which imply that the regression function is smooth over high-density clusters or manifolds supporting the data. A generalization of these assumptions is that the regression function is smooth with respect to some density sensitive distance. This motivates the use of a density based metric for semisupervised learning. We analyze this setting and make the following contributions - (a) we propose a semi-supervised learner that uses a density-sensitive kernel and show that it provides better performance than any supervised learner if the density support set has a small condition number and (b) we show that it is possible to adapt to the degree of semi-supervisedness using data-dependent choice of a parameter that controls sensitivity of the distance metric to the density. This ensures that the semisupervis...
Nonlinear Modeling and Neuro-Fuzzy Control of PEMFC
Institute of Scientific and Technical Information of China (English)
无
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.
Simulink-based HW/SW codesign of embedded neuro-fuzzy systems.
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.
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.
A Neuro-Fuzzy Approach for Modelling Electricity Demand in Victoria
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) ...
NEURO FUZZY LINK BASED CLASSIFIER FOR THE ANALYSIS OF BEHAVIOR MODELS IN SOCIAL NETWORKS
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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.
A hybrid adaptive control strategy for a smart prosthetic hand.
Chen, Cheng-Hung; Naidu, D Subbaram; Perez-Gracia, Alba; Schoen, Marco P
2009-01-01
This paper presents a hybrid of a soft computing technique of adaptive neuro-fuzzy inference system (ANFIS) and a hard computing technique of adaptive control for a two-dimensional movement of a prosthetic hand with a thumb and index finger. In particular, ANFIS is used for inverse kinematics, and the adaptive control is used for linearized dynamics to minimize tracking error. The simulations of this hybrid controller, when compared with the proportional-integral-derivative (PID) controller showed enhanced performance. Work is in progress to extend this methodology to a five-fingered, three-dimensional prosthetic hand.
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
Feature Selection Based on Adaptive Fuzzy Membership Functions%基于自适应隶属度函数的特征选择
Institute of Scientific and Technical Information of China (English)
谢衍涛; 桑农; 张天序
2006-01-01
Neuro-fuzzy (NF) networks are adaptive fuzzy inference systems (FIS) and have been applied to feature selection by some researchers. However, their rule number will grow exponentially as the data dimension increases. On the other hand, feature selection algorithms with artificial neural networks (ANN) usually require normalization of input data, which will probably change some characteristics of original data that are important for classification. To overcome the problems mentioned above, this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron (MLP) to form a new artificial neural network. Furthermore, fuzzification strategy and feature measurement based on membership space are proposed for feature selection.Finally, experiments with both natural and artificial data are carried out to compare with other methods, and the results approve the validity of the algorithm.
Terrorism Event Classification Using Fuzzy Inference Systems
Inyaem, Uraiwan; Meesad, Phayung; Tran, Dat
2010-01-01
Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using fuzzy inference systems (FISs). Each FIS is a decision-making model combining fuzzy logic and approximate reasoning. It is generated in five main parts: the input interface, the fuzzification interface, knowledge base unit, decision making unit and output defuzzification interface. Adaptive neuro-fuzzy inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic and neural network. The ANFIS utilizes automatic identification of fuzzy logic rules and adjustment of membership function (MF). Moreover, neural network can directly learn from data set to construct fuzzy logic rules and MF implemented in various applications. FIS settings are evaluated based on two comparisons. The first evaluat...
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
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.
A Neuro-Fuzzy based System for Classification of Natural Textures
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.
Neuro-fuzzy quantification of personal perceptions of facial images based on a limited data set.
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.
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.
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.
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.
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.
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.
A neuro-fuzzy architecture for real-time applications
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.
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.
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.
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.
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.
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.
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.
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)
Prediction of photonic crystal fiber characteristics by Neuro-Fuzzy system
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.
Neuro fuzzy force control for soft dry contact Hertzian ultrasonic probe
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.
Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach.
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.
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.
FPGA implementation of neuro-fuzzy system with improved PSO learning.
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.
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.
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.
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.
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.
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.
An Improvement of Empirical Risk Functional in Neuro-Fuzzy Classifier
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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
NF-SAVO: Neuro-Fuzzy system for Arabic Video OCR
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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.
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
Performance analysis of electronic power transformer based on neuro-fuzzy controller.
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.
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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.
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A. R Abdollahnejad Barough
2016-04-01
. Finally, a total amount of the second moment (m2 and matrix vectors of image were selected as features. Features and rules produced from decision tree fed into an Adaptable Neuro-fuzzy Inference System (ANFIS. ANFIS provides a neural network based on Fuzzy Inference System (FIS can produce appropriate output corresponding input patterns. Results and Discussion: The proposed model was trained and tested inside ANFIS Editor of the MATLAB software. 300 images, including closed shell, pithy and empty pistachio were selected for training and testing. This network uses 200 data related to these two features and were trained over 200 courses, the accuracy of the result was 95.8%. 100 image have been used to test network over 40 courses with accuracy 97%. The time for the training and testing steps are 0.73 and 0.31 seconds, respectively, and the time to choose the features and rules was 2.1 seconds. Conclusions: In this study, a model was introduced to sort non- split nuts, blank nuts and filled nuts pistachios. Evaluation of training and testing, shows that the model has the ability to classify different types of nuts with high precision. In the previously proposed methods, merely non-split and split pistachio nuts were sorted and being filled or blank nuts is unrecognizable. Nevertheless, accuracy of the mentioned method is 95.56 percent. As well as, other method sorted non-split and split pistachio nuts with an accuracy of 98% and 85% respectively for training and testing steps. The model proposed in this study is better than the other methods and it is encouraging for the improvement and development of the model.
Short-term and long-term thermal prediction of a walking beam furnace using neuro-fuzzy techniques
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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.
Statistical Inference for Data Adaptive Target Parameters.
Hubbard, Alan E; Kherad-Pajouh, Sara; van der Laan, Mark J
2016-05-01
Consider one observes n i.i.d. copies of a random variable with a probability distribution that is known to be an element of a particular statistical model. In order to define our statistical target we partition the sample in V equal size sub-samples, and use this partitioning to define V splits in an estimation sample (one of the V subsamples) and corresponding complementary parameter-generating sample. For each of the V parameter-generating samples, we apply an algorithm that maps the sample to a statistical target parameter. We define our sample-split data adaptive statistical target parameter as the average of these V-sample specific target parameters. We present an estimator (and corresponding central limit theorem) of this type of data adaptive target parameter. This general methodology for generating data adaptive target parameters is demonstrated with a number of practical examples that highlight new opportunities for statistical learning from data. This new framework provides a rigorous statistical methodology for both exploratory and confirmatory analysis within the same data. Given that more research is becoming "data-driven", the theory developed within this paper provides a new impetus for a greater involvement of statistical inference into problems that are being increasingly addressed by clever, yet ad hoc pattern finding methods. To suggest such potential, and to verify the predictions of the theory, extensive simulation studies, along with a data analysis based on adaptively determined intervention rules are shown and give insight into how to structure such an approach. The results show that the data adaptive target parameter approach provides a general framework and resulting methodology for data-driven science.
Use of an adaptive neuro-fuzzy system to characterize root distribution patterns
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...
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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
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.
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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.
Network inference via adaptive optimal design
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Stigter Johannes D
2012-09-01
Full Text Available Abstract Background Current research in network reverse engineering for genetic or metabolic networks very often does not include a proper experimental and/or input design. In this paper we address this issue in more detail and suggest a method that includes an iterative design of experiments based, on the most recent data that become available. The presented approach allows a reliable reconstruction of the network and addresses an important issue, i.e., the analysis and the propagation of uncertainties as they exist in both the data and in our own knowledge. These two types of uncertainties have their immediate ramifications for the uncertainties in the parameter estimates and, hence, are taken into account from the very beginning of our experimental design. Findings The method is demonstrated for two small networks that include a genetic network for mRNA synthesis and degradation and an oscillatory network describing a molecular network underlying adenosine 3’-5’ cyclic monophosphate (cAMP as observed in populations of Dyctyostelium cells. In both cases a substantial reduction in parameter uncertainty was observed. Extension to larger scale networks is possible but needs a more rigorous parameter estimation algorithm that includes sparsity as a constraint in the optimization procedure. Conclusion We conclude that a careful experiment design very often (but not always pays off in terms of reliability in the inferred network topology. For large scale networks a better parameter estimation algorithm is required that includes sparsity as an additional constraint. These algorithms are available in the literature and can also be used in an adaptive optimal design setting as demonstrated in this paper.
Tomato grading system using machine vision technology and neuro-fuzzy networks (ANFIS
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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
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.
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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.
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
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.
Utilizing a Magnetic Abrasive Finishing Technique (MAF Via Adaptive Nero Fuzzy(ANFIS
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Amer A. Moosa
2015-07-01
Full Text Available An experimental study was conducted for measuring the quality of surface finishing roughness using magnetic abrasive finishing technique (MAF on brass plate which is very difficult to be polish by a conventional machining process where the cost is high and much more susceptible to surface damage as compared to other materials. Four operation parameters were studied, the gap between the work piece and the electromagnetic inductor, the current that generate the flux, the rotational Spindale speed and amount of abrasive powder size considering constant linear feed movement between machine head and workpiece. Adaptive Neuro fuzzy inference system (ANFIS was implemented for evaluation of a series of experiments and a verification with respect to specimen roughness change has been optimized and usefully achieved by obtained results were an average of the error between the surface roughness predicted by model simulation and that of direct measure is 2.0222 %.
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。
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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.
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.
Neuro-fuzzy and model-based motion control for mobile manipulator among dynamic obstacles
Institute of Scientific and Technical Information of China (English)
无
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.
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.
Do people treat missing information adaptively when making inferences?
Garcia-Retamero, Rocio; Rieskamp, Jörg
2009-10-01
When making inferences, people are often confronted with situations with incomplete information. Previous research has led to a mixed picture about how people react to missing information. Options include ignoring missing information, treating it as either positive or negative, using the average of past observations for replacement, or using the most frequent observation of the available information as a placeholder. The accuracy of these inference mechanisms depends on characteristics of the environment. When missing information is uniformly distributed, it is most accurate to treat it as the average, whereas when it is negatively correlated with the criterion to be judged, treating missing information as if it were negative is most accurate. Whether people treat missing information adaptively according to the environment was tested in two studies. The results show that participants were sensitive to how missing information was distributed in an environment and most frequently selected the mechanism that was most adaptive. From these results the authors conclude that reacting to missing information in different ways is an adaptive response to environmental characteristics.
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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.
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.
Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data
Cocco, Simona
2011-01-01
We introduce a procedure to infer the interactions among a set of binary variables, based on their sampled frequencies and pairwise correlations. The algorithm builds the clusters of variables contributing most to the entropy of the inferred Ising model, and rejects the small contributions due to the sampling noise. Our procedure successfully recovers benchmark Ising models even at criticality and in the low temperature phase, and is applied to neurobiological data.
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...
Some challenges with statistical inference in adaptive designs.
Hung, H M James; Wang, Sue-Jane; Yang, Peiling
2014-01-01
Adaptive designs have generated a great deal of attention to clinical trial communities. The literature contains many statistical methods to deal with added statistical uncertainties concerning the adaptations. Increasingly encountered in regulatory applications are adaptive statistical information designs that allow modification of sample size or related statistical information and adaptive selection designs that allow selection of doses or patient populations during the course of a clinical trial. For adaptive statistical information designs, a few statistical testing methods are mathematically equivalent, as a number of articles have stipulated, but arguably there are large differences in their practical ramifications. We pinpoint some undesirable features of these methods in this work. For adaptive selection designs, the selection based on biomarker data for testing the correlated clinical endpoints may increase statistical uncertainty in terms of type I error probability, and most importantly the increased statistical uncertainty may be impossible to assess.
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)
DEFF Research Database (Denmark)
Møller, Jesper
.1 with the title ‘Inference'.) This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods using Markov chain Monte Carlo (MCMC) simulations. Due to space limitations the focus...
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.
Inference in Adaptive Regression via the Kac-Rice Formula
2014-05-15
distribution, the Tracy- Widom law Johnstone (2001). In this case, our test statistic is based on the conditional distribution of λ1 given λ2...Tracy- Widom limit. The simulation is the work of Yunjin Choi, currently a Ph.D. student in- vestigating the use of the Kac-Rice pivot to inference in...PCA. The example is a rank-one example, demonstrating that the Kac-Rice test is competitive with the Tracy- Widom approximation of Johnstone (2001
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.
An Adaptive Hybrid Multi-level Intelligent Intrusion Detection System for Network Security
Directory of Open Access Journals (Sweden)
P. Ananthi
2014-04-01
Full Text Available Intrusion Detection System (IDS plays a vital factor in providing security to the networks through detecting malicious activities. Due to the extensive advancements in the computer networking, IDS has become an active area of research to determine various types of attacks in the networks. A large number of intrusion detection approaches are available in the literature using several traditional statistical and data mining approaches. Data mining techniques in IDS observed to provide significant results. Data mining approaches for misuse and anomaly-based intrusion detection generally include supervised, unsupervised and outlier approaches. It is important that the efficiency and potential of IDS be updated based on the criteria of new attacks. This study proposes a novel Adaptive Hybrid Multi-level Intelligent IDS (AHMIIDS system which is the combined version of anomaly and misuse detection techniques. The anomaly detection is based on Bayesian Networks and then the misuse detection is performed using Adaptive Neuro Fuzzy Inference System (ANFIS. The outputs of both anomaly detection and misuse detection modules are applied to Decision Table Majority (DTM to perform the final decision making. A rule-base approach is used in this system. It is observed from the results that the proposed AHMIIDS performs better than other conventional hybrid IDS.
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.
DEFF Research Database (Denmark)
Møller, Jesper
2010-01-01
Chapter 9: This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods based on a maximum likelihood or Bayesian approach combined with markov chain Monte Carlo...
On-Line Real Time Realization and Application of Adaptive Fuzzy Inference Neural Network
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
In this paper,a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm,combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear mu Iti-variable systems is introduced and discussed.
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...
Adaptive PID control based on orthogonal endocrine neural networks.
Milovanović, Miroslav B; Antić, Dragan S; Milojković, Marko T; Nikolić, Saša S; Perić, Staniša Lj; Spasić, Miodrag D
2016-12-01
A new intelligent hybrid structure used for online tuning of a PID controller is proposed in this paper. The structure is based on two adaptive neural networks, both with built-in Chebyshev orthogonal polynomials. First substructure network is a regular orthogonal neural network with implemented artificial endocrine factor (OENN), in the form of environmental stimuli, to its weights. It is used for approximation of control signals and for processing system deviation/disturbance signals which are introduced in the form of environmental stimuli. The output values of OENN are used to calculate artificial environmental stimuli (AES), which represent required adaptation measure of a second network-orthogonal endocrine adaptive neuro-fuzzy inference system (OEANFIS). OEANFIS is used to process control, output and error signals of a system and to generate adjustable values of proportional, derivative, and integral parameters, used for online tuning of a PID controller. The developed structure is experimentally tested on a laboratory model of the 3D crane system in terms of analysing tracking performances and deviation signals (error signals) of a payload. OENN-OEANFIS performances are compared with traditional PID and 6 intelligent PID type controllers. Tracking performance comparisons (in transient and steady-state period) showed that the proposed adaptive controller possesses performances within the range of other tested controllers. The main contribution of OENN-OEANFIS structure is significant minimization of deviation signals (17%-79%) compared to other controllers. It is recommended to exploit it when dealing with a highly nonlinear system which operates in the presence of undesirable disturbances.
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.
Specificity and timescales of cortical adaptation as inferences about natural movie statistics.
Snow, Michoel; Coen-Cagli, Ruben; Schwartz, Odelia
2016-10-01
Adaptation is a phenomenological umbrella term under which a variety of temporal contextual effects are grouped. Previous models have shown that some aspects of visual adaptation reflect optimal processing of dynamic visual inputs, suggesting that adaptation should be tuned to the properties of natural visual inputs. However, the link between natural dynamic inputs and adaptation is poorly understood. Here, we extend a previously developed Bayesian modeling framework for spatial contextual effects to the temporal domain. The model learns temporal statistical regularities of natural movies and links these statistics to adaptation in primary visual cortex via divisive normalization, a ubiquitous neural computation. In particular, the model divisively normalizes the present visual input by the past visual inputs only to the degree that these are inferred to be statistically dependent. We show that this flexible form of normalization reproduces classical findings on how brief adaptation affects neuronal selectivity. Furthermore, prior knowledge acquired by the Bayesian model from natural movies can be modified by prolonged exposure to novel visual stimuli. We show that this updating can explain classical results on contrast adaptation. We also simulate the recent finding that adaptation maintains population homeostasis, namely, a balanced level of activity across a population of neurons with different orientation preferences. Consistent with previous disparate observations, our work further clarifies the influence of stimulus-specific and neuronal-specific normalization signals in adaptation.
Specificity and timescales of cortical adaptation as inferences about natural movie statistics
Snow, Michoel; Coen-Cagli, Ruben; Schwartz, Odelia
2016-01-01
Adaptation is a phenomenological umbrella term under which a variety of temporal contextual effects are grouped. Previous models have shown that some aspects of visual adaptation reflect optimal processing of dynamic visual inputs, suggesting that adaptation should be tuned to the properties of natural visual inputs. However, the link between natural dynamic inputs and adaptation is poorly understood. Here, we extend a previously developed Bayesian modeling framework for spatial contextual effects to the temporal domain. The model learns temporal statistical regularities of natural movies and links these statistics to adaptation in primary visual cortex via divisive normalization, a ubiquitous neural computation. In particular, the model divisively normalizes the present visual input by the past visual inputs only to the degree that these are inferred to be statistically dependent. We show that this flexible form of normalization reproduces classical findings on how brief adaptation affects neuronal selectivity. Furthermore, prior knowledge acquired by the Bayesian model from natural movies can be modified by prolonged exposure to novel visual stimuli. We show that this updating can explain classical results on contrast adaptation. We also simulate the recent finding that adaptation maintains population homeostasis, namely, a balanced level of activity across a population of neurons with different orientation preferences. Consistent with previous disparate observations, our work further clarifies the influence of stimulus-specific and neuronal-specific normalization signals in adaptation. PMID:27699416
Prudhomme, Serge
2015-09-17
Parameter estimation for complex models using Bayesian inference is usually a very costly process as it requires a large number of solves of the forward problem. We show here how the construction of adaptive surrogate models using a posteriori error estimates for quantities of interest can significantly reduce the computational cost in problems of statistical inference. As surrogate models provide only approximations of the true solutions of the forward problem, it is nevertheless necessary to control these errors in order to construct an accurate reduced model with respect to the observables utilized in the identification of the model parameters. Effectiveness of the proposed approach is demonstrated on a numerical example dealing with the Spalart–Allmaras model for the simulation of turbulent channel flows. In particular, we illustrate how Bayesian model selection using the adapted surrogate model in place of solving the coupled nonlinear equations leads to the same quality of results while requiring fewer nonlinear PDE solves.
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.
Adaptive Quantization Index Modulation Audio Watermarking based on Fuzzy Inference System
Directory of Open Access Journals (Sweden)
Sunita V. Dhavale
2014-02-01
Full Text Available Many of the adaptive watermarking schemes reported in the literature consider only local audio signal properties. Many schemes require complex computation along with manual parameter settings. In this paper, we propose a novel, fuzzy, adaptive audio watermarking algorithm based on both global and local audio signal properties. The algorithm performs well for dynamic range of audio signals without requiring manual initial parameter selection. Here, mean value of energy (MVE and variance of spectral flux (VSF of a given audio signal constitutes global components, while the energy of each audio frame acts as local component. The Quantization Index Modulation (QIM step size Δ is made adaptive to both the global and local features. The global component automates the initial selection of Δ using the fuzzy inference system while the local component controls the variation in it based on the energy of individual audio frame. Hence Δ adaptively controls the strength of watermark to meet both the robustness and inaudibility requirements, making the system independent of audio nature. Experimental results reveal that our adaptive scheme outperforms other fixed step sized QIM schemes and adaptive schemes and is highly robust against general attacks.
Zhu, Hongjian
2016-12-12
Seamless phase II/III clinical trials have attracted increasing attention recently. They mainly use Bayesian response adaptive randomization (RAR) designs. There has been little research into seamless clinical trials using frequentist RAR designs because of the difficulty in performing valid statistical inference following this procedure. The well-designed frequentist RAR designs can target theoretically optimal allocation proportions, and they have explicit asymptotic results. In this paper, we study the asymptotic properties of frequentist RAR designs with adjusted target allocation proportions, and investigate statistical inference for this procedure. The properties of the proposed design provide an important theoretical foundation for advanced seamless clinical trials. Our numerical studies demonstrate that the design is ethical and efficient.
Using adaptive network based fuzzy inference system to forecast regional electricity loads
Energy Technology Data Exchange (ETDEWEB)
Ying, Li-Chih [Department of Marketing Management, Central Taiwan University of Science and Technology, 11, Pu-tzu Lane, Peitun, Taichung City 406 (China); Pan, Mei-Chiu [Graduate Institute of Management Sciences, Nanhua University, 32, Chung Keng Li, Dalin, Chiayi 622 (China)
2008-02-15
Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads. (author)
Hu, Zixi; Yao, Zhewei; Li, Jinglai
2017-03-01
Many scientific and engineering problems require to perform Bayesian inference for unknowns of infinite dimension. In such problems, many standard Markov Chain Monte Carlo (MCMC) algorithms become arbitrary slow under the mesh refinement, which is referred to as being dimension dependent. To this end, a family of dimensional independent MCMC algorithms, known as the preconditioned Crank-Nicolson (pCN) methods, were proposed to sample the infinite dimensional parameters. In this work we develop an adaptive version of the pCN algorithm, where the covariance operator of the proposal distribution is adjusted based on sampling history to improve the simulation efficiency. We show that the proposed algorithm satisfies an important ergodicity condition under some mild assumptions. Finally we provide numerical examples to demonstrate the performance of the proposed method.
Directory of Open Access Journals (Sweden)
C. Muniraj
2011-01-01
Full Text Available This paper presents the prediction of pollution severity of the polymeric insulators used in power transmission lines using adaptive neurofuzzy inference system (ANFIS model. In this work, laboratory-based pollution performance tests were carried out on 11 kV silicone rubber polymeric insulator under AC voltage at different pollution levels with sodium chloride as a contaminant. Leakage current was measured during the laboratory tests. Time domain and frequency domain characteristics of leakage current, such as mean value, maximum value, standard deviation, and total harmonics distortion (THD, have been extracted, which jointly describe the pollution severity of the polymeric insulator surface. Leakage current characteristics are used as the inputs of ANFIS model. The pollution severity index “equivalent salt deposit density” (ESDD is used as the output of the proposed model. Results of the research can give sufficient prewarning time before pollution flashover and help in the condition based maintenance (CBM chart preparation.
Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J
2014-01-01
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.
Directory of Open Access Journals (Sweden)
Yanlei Li
2015-01-01
Full Text Available This paper proposes a new method for predicting spindle deformation based on temperature data. The method introduces the adaptive neurofuzzy inference system (ANFIS, which is a neurofuzzy modeling approach that integrates the kernel and geometrical transformations. By utilizing data transformation, the number of ANFIS rules can be effectively reduced and the predictive model structure can be simplified. To build the predictive model, we first map the original temperature data to a feature space with Gaussian kernels. We then process the mapped data with the geometrical transformation and make the data gather in the square region. Finally, the transformed data are used as input to train the ANFIS. A verification experiment is conducted to evaluate the performance of the proposed method. Six Pt100 thermal resistances are used to monitor the spindle temperature, and a laser displacement sensor is used to detect the spindle deformation. Experimental results show that the proposed method can precisely predict the spindle deformation and greatly improve the thermal performance of the spindle. Compared with back propagation (BP networks, the proposed method is more suitable for complex working conditions in practical applications.
Directory of Open Access Journals (Sweden)
Savić Marija
2014-01-01
Full Text Available This paper presents the results of the tropospheric ozone concentration modeling as the dependence on volatile organic compounds - VOCs (Benzene, Toluene, m,p-Xylene, o-Xylene, Ethylbenzene; nonorganic compounds - NOx (NO, NO2, NOx, CO, H2S, SO2 and PM10 in the ambient air in parallel with the meteorological parameters: temperature, solar radiation, relative humidity, wind speed and direction. Modeling is based on measured results obtained during the year 2009. The measurements were performed at the measuring station located within an agricultural area, in vicinity of city of Zrenjanin (Serbian Banat, Serbia. Statistical analysis of obtained data, based on bivariate correlation analysis indicated that accurate modeling cannot be performed using linear statistics approach. Also, considering that almost all input variables have wide range of relative change (ratio of variance compared to range, nonlinear statistic analysis method based on only one rule describing the behavior of input variable, most certainly wouldn’t present accurate enough results. From that reason, modeling approach was based on Adaptive-Network-Based Fuzzy Inference System (ANFIS. Model obtained using ANFIS methodology resulted with high accuracy, with prediction potential of above 80%, considering that obtained determination coefficient for the final model was R2=0.802.
PHONETIC CLASSIFICATION BY ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM AND SUBTRACTIVE CLUSTERING
Directory of Open Access Journals (Sweden)
Samiya Silarbi
2014-09-01
Full Text Available This paper presents the application of Adaptive Network Based Fuzzy Inference System ANFIS on speech recognition. The primary tasks of fuzzy modeling are structure identification and parameter optimization, the former determines the numbers of membership functions and fuzzy if-then rules while the latter identifies a feasible set of parameters under the given structure. However, the increase of input dimension, rule numbers will have an exponential growth and there will cause problem of “rule disaster”. Thus, determination of an appropriate structure becomes an important issue where subtractive clustering is applied to define an optimal initial structure and obtain small number of rules. The appropriate learning algorithm is performed on TIMIT speech database supervised type, a pre-processing of the acoustic signal and extracting the coefficients MFCCs parameters relevant to the recognition system. Finally, hybrid learning combines the gradient decent and least square estimation LSE of parameters network. The results obtained show the effectiveness of the method in terms of recognition rate and number of fuzzy rules generated.
Accardi, Luigi; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro
2016-07-01
Recently a novel quantum information formalism — quantum adaptive dynamics — was developed and applied to modelling of information processing by bio-systems including cognitive phenomena: from molecular biology (glucose-lactose metabolism for E.coli bacteria, epigenetic evolution) to cognition, psychology. From the foundational point of view quantum adaptive dynamics describes mutual adapting of the information states of two interacting systems (physical or biological) as well as adapting of co-observations performed by the systems. In this paper we apply this formalism to model unconscious inference: the process of transition from sensation to perception. The paper combines theory and experiment. Statistical data collected in an experimental study on recognition of a particular ambiguous figure, the Schröder stairs, support the viability of the quantum(-like) model of unconscious inference including modelling of biases generated by rotation-contexts. From the probabilistic point of view, we study (for concrete experimental data) the problem of contextuality of probability, its dependence on experimental contexts. Mathematically contextuality leads to non-Komogorovness: probability distributions generated by various rotation contexts cannot be treated in the Kolmogorovian framework. At the same time they can be embedded in a “big Kolmogorov space” as conditional probabilities. However, such a Kolmogorov space has too complex structure and the operational quantum formalism in the form of quantum adaptive dynamics simplifies the modelling essentially.
Adaptive fuzzy control of underactuated robotic systems with the use of differential flatness theory
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.
ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers
César, Manuel Braz; Barros, Rui Carneiro
2016-11-01
In this paper, we report on the development of a neuro-fuzzy controller for magnetorheological dampers using an Adaptive Neuro-Fuzzy Inference System or ANFIS. Fuzzy logic based controllers are capable to deal with non-linear or uncertain systems, which make them particularly well suited for civil engineering applications. The main objective is to develop a semi-active control system with a MR damper to reduce the response of a three degrees-of-freedom (DOFs) building structure. The control system is designed using ANFIS to optimize the fuzzy inference rule of a simple fuzzy logic controller. The results show that the proposed semi-active neuro-fuzzy based controller is effective in reducing the response of structural system.
Khan, Laiq
2017-01-01
The hybrid power system (HPS) is an emerging power generation scheme due to the plentiful availability of renewable energy sources. Renewable energy sources are characterized as highly intermittent in nature due to meteorological conditions, while the domestic load also behaves in a quite uncertain manner. In this scenario, to maintain the balance between generation and load, the development of an intelligent and adaptive control algorithm has preoccupied power engineers and researchers. This paper proposes a Hermite wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control of photovoltaic (PV) systems to extract maximum power and a Hermite wavelet incorporated NeuroFuzzy indirect adaptive control of Solid Oxide Fuel Cells (SOFC) to obtain a swift response in a grid-connected hybrid power system. A comprehensive simulation testbed for a grid-connected hybrid power system (wind turbine, PV cells, SOFC, electrolyzer, battery storage system, supercapacitor (SC), micro-turbine (MT) and domestic load) is developed in Matlab/Simulink. The robustness and superiority of the proposed indirect adaptive control paradigm are evaluated through simulation results in a grid-connected hybrid power system testbed by comparison with a conventional PI (proportional and integral) control system. The simulation results verify the effectiveness of the proposed control paradigm. PMID:28329015
Mumtaz, Sidra; Khan, Laiq
2017-01-01
The hybrid power system (HPS) is an emerging power generation scheme due to the plentiful availability of renewable energy sources. Renewable energy sources are characterized as highly intermittent in nature due to meteorological conditions, while the domestic load also behaves in a quite uncertain manner. In this scenario, to maintain the balance between generation and load, the development of an intelligent and adaptive control algorithm has preoccupied power engineers and researchers. This paper proposes a Hermite wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control of photovoltaic (PV) systems to extract maximum power and a Hermite wavelet incorporated NeuroFuzzy indirect adaptive control of Solid Oxide Fuel Cells (SOFC) to obtain a swift response in a grid-connected hybrid power system. A comprehensive simulation testbed for a grid-connected hybrid power system (wind turbine, PV cells, SOFC, electrolyzer, battery storage system, supercapacitor (SC), micro-turbine (MT) and domestic load) is developed in Matlab/Simulink. The robustness and superiority of the proposed indirect adaptive control paradigm are evaluated through simulation results in a grid-connected hybrid power system testbed by comparison with a conventional PI (proportional and integral) control system. The simulation results verify the effectiveness of the proposed control paradigm.
Perspectives of Probabilistic Inferences: Reinforcement Learning and an Adaptive Network Compared
Rieskamp, Jorg
2006-01-01
The assumption that people possess a strategy repertoire for inferences has been raised repeatedly. The strategy selection learning theory specifies how people select strategies from this repertoire. The theory assumes that individuals select strategies proportional to their subjective expectations of how well the strategies solve particular…
Soil disturbance evaluation: application of ANFIS
New techniques to understand the relationship of soil components as impacted by management are needed. In this work, an Adaptive Neuro-Fuzzy Inference System (ANFIS) applied for study the contiguous relations between soil disturbed indicators. Several ANFIS surfaces, which described the contiguous ...
Erratum: Erratum to Central European Journal of Engineering, Volume 4, Issue 1
Kumar, M. Ajay; Srikanth, N. V.
2014-06-01
Paper by M. Ajay Kumar, N. V. Srikanth, et al. "An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions" in Volume 4, Issue 1, 27-38/March 2014 doi: 10.2478/s13531-013-0143-4 contains an error in the title. The correct title is presented below
Erratum to Central European Journal of Engineering, Volume 4, Issue 1
Kumar, M.; Srikanth, N.
2014-06-01
Paper by M. Ajay Kumar, N. V. Srikanth, et al. "An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions" in Volume 4, Issue 1, 27-38/March 2014 doi: 10.2478/s13531-013-0143-4 contains an error in the title. The correct title is presented below
Kiso, Atsushi; Seki, Hirokazu
This paper describes a method for discriminating of the human forearm motions based on the myoelectric signals using an adaptive fuzzy inference system. In conventional studies, the neural network is often used to estimate motion intention by the myoelectric signals and realizes the high discrimination precision. On the other hand, this study uses the fuzzy inference for a human forearm motion discrimination based on the myoelectric signals. This study designs the membership function and the fuzzy rules using the average value and the standard deviation of the root mean square of the myoelectric potential for every channel of each motion. In addition, the characteristics of the myoelectric potential gradually change as a result of the muscle fatigue. Therefore, the motion discrimination should be performed by taking muscle fatigue into consideration. This study proposes a method to redesign the fuzzy inference system such that dynamic change of the myoelectric potential because of the muscle fatigue will be taken into account. Some experiments carried out using a myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.
Adaptive fuzzy control with output feedback for H infinity tracking of SISO nonlinear systems.
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.
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
Institute of Scientific and Technical Information of China (English)
曹政才; 邓积杰; 刘民; 王永吉
2012-01-01
Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.
Adaptive thresholding for reliable topological inference in single subject fMRI analysis
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Krzysztof eGorgolewski
2012-08-01
Full Text Available Single subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumour resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However, because current thresholding methods were developed mainly in the context of cognitive neuroscience group studies, most single subject fMRI maps are thresholded manually to satisfy specific criteria related to single subject analyses. Here, we propose a new adaptive thresholding method which combines Gamma-Gaussian mixture modelling with topological thresholding to improve cluster delineation. In a series of simulations we show that by adapting to the signal and noise properties, the new method performs well in terms of the trade-off between false negative and positive cluster error rates as well as in terms of over and underestimation of the true activation border. We also show through simulations and a motor test-retest study on ten volunteer subjects that adaptive thresholding improves reliability, mainly by accounting for the global signal variance. This in turn increases the likelihood that the true activation pattern can be determined.
Energy Technology Data Exchange (ETDEWEB)
Zhang, Guannan [ORNL; Webster, Clayton G [ORNL; Gunzburger, Max D [ORNL
2012-09-01
Although Bayesian analysis has become vital to the quantification of prediction uncertainty in groundwater modeling, its application has been hindered due to the computational cost associated with numerous model executions needed for exploring the posterior probability density function (PPDF) of model parameters. This is particularly the case when the PPDF is estimated using Markov Chain Monte Carlo (MCMC) sampling. In this study, we develop a new approach that improves computational efficiency of Bayesian inference by constructing a surrogate system based on an adaptive sparse-grid high-order stochastic collocation (aSG-hSC) method. Unlike previous works using first-order hierarchical basis, we utilize a compactly supported higher-order hierar- chical basis to construct the surrogate system, resulting in a significant reduction in the number of computational simulations required. In addition, we use hierarchical surplus as an error indi- cator to determine adaptive sparse grids. This allows local refinement in the uncertain domain and/or anisotropic detection with respect to the random model parameters, which further improves computational efficiency. Finally, we incorporate a global optimization technique and propose an iterative algorithm for building the surrogate system for the PPDF with multiple significant modes. Once the surrogate system is determined, the PPDF can be evaluated by sampling the surrogate system directly with very little computational cost. The developed method is evaluated first using a simple analytical density function with multiple modes and then using two synthetic groundwater reactive transport models. The groundwater models represent different levels of complexity; the first example involves coupled linear reactions and the second example simulates nonlinear ura- nium surface complexation. The results show that the aSG-hSC is an effective and efficient tool for Bayesian inference in groundwater modeling in comparison with conventional
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Jan eKneissler
2015-04-01
Full Text Available Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF. PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than ten-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.
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Ying-Yi Hong
2014-04-01
Full Text Available Microgrids are a highly efficient means of embedding distributed generation sources in a power system. However, if a fault occurs inside or outside the microgrid, the microgrid should be immediately disconnected from the main grid using a static switch installed at the secondary side of the main transformer near the point of common coupling (PCC. The static switch should have a reliable module implemented in a chip to detect/locate the fault and activate the breaker to open the circuit immediately. This paper proposes a novel approach to design this module in a static switch using the discrete wavelet transform (DWT and adaptive network-based fuzzy inference system (ANFIS. The wavelet coefficient of the fault voltage and the inference results of ANFIS with the wavelet energy of the fault current at the secondary side of the main transformer determine the control action (open or close of a static switch. The ANFIS identifies the faulty zones inside or outside the microgrid. The proposed method is applied to the first outdoor microgrid test bed in Taiwan, with a generation capacity of 360.5 kW. This microgrid test bed is studied using the real-time simulator eMegaSim developed by Opal-RT Technology Inc. (Montreal, QC, Canada. The proposed method based on DWT and ANFIS is implemented in a field programmable gate array (FPGA by using the Xilinx System Generator. Simulation results reveal that the proposed method is efficient and applicable in the real-time control environment of a power system.
Grodwohl, Jean-Baptiste
2016-08-01
This paper gives a detailed narrative of a controversial empirical research in postwar population genetics, the analysis of the cytological polymorphisms of an Australian grasshopper, Moraba scurra. This research intertwined key technical developments in three research areas during the 1950s and 1960s: it involved Dobzhansky's empirical research program on cytological polymorphisms, the mathematical theory of natural selection in two-locus systems, and the building of reliable estimates of natural selection in the wild. In the mid-1950s the cytologist Michael White discovered an interesting case of epistasis in populations of Moraba scurra. These observations received a wide diffusion when theoretical population geneticist Richard Lewontin represented White's data on adaptive topographies. These topographies connected the information on the genetic structure of these grasshopper populations with the formal framework of theoretical population genetics. As such, they appeared at the time as the most successful application of two-locus models of natural selection to an empirical study system. However, this connection generated paradoxical results: in the landscapes, all grasshopper populations were located on a ridge (an unstable equilibrium) while they were expected to reach a peak. This puzzling result fueled years of research and triggered a controversy attracting contributors from Australia, the United States and the United Kingdom. While the original problem seemed, at first, purely empirical, the subsequent controversy affected the main mathematical tools used in the study of two-gene systems under natural selection. Adaptive topographies and their underlying mathematical structure, Wright's mean fitness equations, were submitted to close scrutiny. Suspicion eventually shifted to the statistical machinery used in data analysis, reflecting the crucial role of statistical inference in applied population genetics. In the 1950s and 1960s, population geneticists were
Amiri, Mohammad J; Abedi-Koupai, Jahangir; Eslamian, Sayed S; Mousavi, Sayed F; Hasheminejad, Hasti
2013-01-01
To evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) model in estimating the efficiency of Pb (II) ions removal from aqueous solution by ostrich bone ash, a batch experiment was conducted. Five operational parameters including adsorbent dosage (C(s)), initial concentration of Pb (II) ions (C(o)), initial pH, temperature (T) and contact time (t) were taken as the input data and the adsorption efficiency (AE) of bone ash as the output. Based on the 31 different structures, 5 ANFIS models were tested against the measured adsorption efficiency to assess the accuracy of each model. The results showed that ANFIS5, which used all input parameters, was the most accurate (RMSE = 2.65 and R(2) = 0.95) and ANFIS1, which used only the contact time input, was the worst (RMSE = 14.56 and R(2) = 0.46). In ranking the models, ANFIS4, ANFIS3 and ANFIS2 ranked second, third and fourth, respectively. The sensitivity analysis revealed that the estimated AE is more sensitive to the contact time, followed by pH, initial concentration of Pb (II) ions, adsorbent dosage, and temperature. The results showed that all ANFIS models overestimated the AE. In general, this study confirmed the capabilities of ANFIS model as an effective tool for estimation of AE.
Directory of Open Access Journals (Sweden)
Chen Yu-Fan
2006-01-01
Full Text Available An adaptive minimum mean-square error (MMSE array receiver based on the fuzzy-logic recursive least-squares (RLS algorithm is developed for asynchronous DS-CDMA interference suppression in the presence of frequency-selective multipath fading. This receiver employs a fuzzy-logic control mechanism to perform the nonlinear mapping of the squared error and squared error variation, denoted by ( , , into a forgetting factor . For the real-time applicability, a computationally efficient version of the proposed receiver is derived based on the least-mean-square (LMS algorithm using the fuzzy-inference-controlled step-size . This receiver is capable of providing both fast convergence/tracking capability as well as small steady-state misadjustment as compared with conventional LMS- and RLS-based MMSE DS-CDMA receivers. Simulations show that the fuzzy-logic LMS and RLS algorithms outperform, respectively, other variable step-size LMS (VSS-LMS and variable forgetting factor RLS (VFF-RLS algorithms at least 3 dB and 1.5 dB in bit-error-rate (BER for multipath fading channels.
Directory of Open Access Journals (Sweden)
Shahab Karimi
2014-01-01
Full Text Available In this study, the effects of ratios of dolomite, base/acid, silica, SiO2/Al2O3, and Fe2O3/CaO, base and acid oxides, and 11 oxides (SiO2, Al2O3, CaO, MgO, MnO, Na2O, K2O, Fe2O3, TiO2, P2O5, and SO3 on ash fusion temperatures for 1040 US coal samples from 12 states were evaluated using regression and adaptive neurofuzzy inference system (ANFIS methods. Different combinations of independent variables were examined to predict ash fusion temperatures in the multivariable procedure. The combination of the “11 oxides + (Base/Acid + Silica ratio” was the best predictor. Correlation coefficients (R2 of 0.891, 0.917, and 0.94 were achieved using nonlinear equations for the prediction of initial deformation temperature (IDT, softening temperature (ST, and fluid temperature (FT, respectively. The mentioned “best predictor” was used as input to the ANFIS system as well, and the correlation coefficients (R2 of the prediction were enhanced to 0.97, 0.98, and 0.99 for IDT, ST, and FT, respectively. The prediction precision that was achieved in this work exceeded that reported in previously published works.
Langmore, Ian; Davis, Anthony B.; Bal, Guillaume; Marzouk, Youssef M.
2012-01-01
We describe a method for accelerating a 3D Monte Carlo forward radiative transfer model to the point where it can be used in a new kind of Bayesian retrieval framework. The remote sensing challenge is to detect and quantify a chemical effluent of a known absorbing gas produced by an industrial facility in a deep valley. The available data is a single low resolution noisy image of the scene in the near IR at an absorbing wavelength for the gas of interest. The detected sunlight has been multiply reflected by the variable terrain and/or scattered by an aerosol that is assumed partially known and partially unknown. We thus introduce a new class of remote sensing algorithms best described as "multi-pixel" techniques that call necessarily for a 3D radaitive transfer model (but demonstrated here in 2D); they can be added to conventional ones that exploit typically multi- or hyper-spectral data, sometimes with multi-angle capability, with or without information about polarization. The novel Bayesian inference methodology uses adaptively, with efficiency in mind, the fact that a Monte Carlo forward model has a known and controllable uncertainty depending on the number of sun-to-detector paths used.
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Jing Li
2017-01-01
Full Text Available The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS model and improved particle swarm optimization (PSO algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.
Age Estimation Based on CLM, Tree Mixture With Adaptive Neuron Fuzzy, Fuzzy Svm
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Mohammad Saber Iraji
2014-02-01
Full Text Available As you know, age diagnosis based on the image is one of the most attractive topics in computer .In this paper, we present a intelligent model to estimate the age of face image. We use shape and texture feature extraction from FG-NET landmark image data set using AAM(Active Appearance Model, CLM (Constrained Local Model, tree Mixture algorithms. Finally, the obtained features were given as the training data to the ANFIS (adaptive neuro fuzzy influence system, FSVM (Fuzzy Support Vector Machine. Our experimental results show that In our proposed system, fuzzy svm has less errors and system worked more accurate and appropriative than prior methods. Our system is able to identify age of face image from different directions as is.
Mekanik, F.; Imteaz, M. A.; Talei, A.
2016-05-01
Accurate seasonal rainfall forecasting is an important step in the development of reliable runoff forecast models. The large scale climate modes affecting rainfall in Australia have recently been proven useful in rainfall prediction problems. In this study, adaptive network-based fuzzy inference systems (ANFIS) models are developed for the first time for southeast Australia in order to forecast spring rainfall. The models are applied in east, center and west Victoria as case studies. Large scale climate signals comprising El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Inter-decadal Pacific Ocean (IPO) are selected as rainfall predictors. Eight models are developed based on single climate modes (ENSO, IOD, and IPO) and combined climate modes (ENSO-IPO and ENSO-IOD). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson correlation coefficient (r) and root mean square error in probability (RMSEP) skill score are used to evaluate the performance of the proposed models. The predictions demonstrate that ANFIS models based on individual IOD index perform superior in terms of RMSE, MAE and r to the models based on individual ENSO indices. It is further discovered that IPO is not an effective predictor for the region and the combined ENSO-IOD and ENSO-IPO predictors did not improve the predictions. In order to evaluate the effectiveness of the proposed models a comparison is conducted between ANFIS models and the conventional Artificial Neural Network (ANN), the Predictive Ocean Atmosphere Model for Australia (POAMA) and climatology forecasts. POAMA is the official dynamic model used by the Australian Bureau of Meteorology. The ANFIS predictions certify a superior performance for most of the region compared to ANN and climatology forecasts. POAMA performs better in regards to RMSE and MAE in east and part of central Victoria, however, compared to ANFIS it shows weaker results in west Victoria in terms of prediction errors and RMSEP skill
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)
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.
Calibration Of U-Tube Manometer Using Frequency Estimation
Roshan Elizabeth Daniel; Anitha Mary.X; R. Jegan; RAJASEKARAN, K.
2013-01-01
U-Tube Manometer is used to measure pressure. It is calibrated using the variation in capacitance. In U-tube manometer, the relation between the level of mercury and the capacitance developed across thecopper plates of the manometer is found to be highly non-linear. Due to its non-predictive nature and nonlinear relationship, artificial intelligence techniques are used to calibrate the system. The artificial intelligence technique used here is Adaptive Neuro-Fuzzy Inference System (ANFIS). Th...
Nero-fuzzy modeling of the convection heat transfer coefficient for the nanofluid
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.
Fuzzy Control Strategies in Human Operator and Sport Modeling
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.
INTELLIGENT DTC FOR PMSM DRIVE USING ANFIS TECHNIQUE
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AHMED A. MAHFOUZ
2012-03-01
Full Text Available This paper describes intelligent direct torque control (DTC technique for Permanent Magnet Synchronous Motor (PMSM drive based on Adaptive Neuro Fuzzy Inference Systems (ANFIS. The proposed system has proven successful in controlling the instantaneous torque so as not to depend only on the estimation flux, torque and position, but also the estimation of the lookup table and the generation of driver switching table. Experimental results prove the MATLAB simulation results for torque, speed and flux estimations.
Application of artificial intelligence models in water quality forecasting.
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.
5th International Conference on Fuzzy and Neuro Computing
Panigrahi, Bijaya; Das, Swagatam; Suganthan, Ponnuthurai
2015-01-01
This proceedings bring together contributions from researchers from academia and industry to report the latest cutting edge research made in the areas of Fuzzy Computing, Neuro Computing and hybrid Neuro-Fuzzy Computing in the paradigm of Soft Computing. The FANCCO 2015 conference explored new application areas, design novel hybrid algorithms for solving different real world application problems. After a rigorous review of the 68 submissions from all over the world, the referees panel selected 27 papers to be presented at the Conference. The accepted papers have a good, balanced mix of theory and applications. The techniques ranged from fuzzy neural networks, decision trees, spiking neural networks, self organizing feature map, support vector regression, adaptive neuro fuzzy inference system, extreme learning machine, fuzzy multi criteria decision making, machine learning, web usage mining, Takagi-Sugeno Inference system, extended Kalman filter, Goedel type logic, fuzzy formal concept analysis, biclustering e...
Reckien, D.
2012-12-01
Emerging and developing economies are currently undergoing one of the profoundest socio-spatial transitions in their history, with strong urbanization and weather extremes bringing about changes in the economy, forms of living and living conditions, but also increasing risks and altered social divides. The impacts of heat waves and strong rain events are therefore differently perceived among urban residents. Addressing the social differences of climate change impacts1 and expanding targeted adaptation options have emerged as urgent policy priorities, particularly for developing and emerging economies2. This paper discusses the perceived impacts of weather-related extreme events on different social groups in New Delhi and Hyderabad, India. Using network statistics and scenario analysis on Fuzzy Cognitive Maps (FCMs) as part of a vulnerability analysis, the investigation provides quantitative and qualitative measures to compare impacts and adaptation strategies for different social groups. Impacts of rain events are stronger than those of heat in both cities and affect the lower income classes particularly. Interestingly, the scenario analysis (comparing altered networks in which the alteration represents a possible adaptation measure) shows that investments in the water infrastructure would be most meaningful and more effective than investments in, e.g., the traffic infrastructure, despite the stronger burden from traffic disruptions and the resulting concentration of planning and policy on traffic ease and investments. The method of Fuzzy Cognitive Mapping offers a link between perception and modeling, and the possibility to aggregate and analyze the views of a large number of stakeholders. Our research has shown that planners and politicians often know about many of the problems, but are often overwhelmed by the problems in their respective cities and look for a prioritization of adaptation options. FCM provides this need and identifies priority adaptation options
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)
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.
Skin Cancer Recognition by Using a Neuro-Fuzzy System
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) ...
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.
Classification of Sleep Stages in Infants: A Neuro Fuzzy Approach
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
Directory of Open Access Journals (Sweden)
Daniel Pincheira-Donoso
Full Text Available Large-scale patterns of current species geographic range-size variation reflect historical dynamics of dispersal and provide insights into future consequences under changing environments. Evidence suggests that climate warming exerts major damage on high latitude and elevation organisms, where changes are more severe and available space to disperse tracking historical niches is more limited. Species with longer generations (slower adaptive responses, such as vertebrates, and with restricted distributions (lower genetic diversity, higher inbreeding in these environments are expected to be particularly threatened by warming crises. However, a well-known macroecological generalization (Rapoport's rule predicts that species range-sizes increase with increasing latitude-elevation, thus counterbalancing the impact of climate change. Here, I investigate geographic range-size variation across an extreme environmental gradient and as a function of body size, in the prominent Liolaemus lizard adaptive radiation. Conventional and phylogenetic analyses revealed that latitudinal (but not elevational ranges significantly decrease with increasing latitude-elevation, while body size was unrelated to range-size. Evolutionarily, these results are insightful as they suggest a link between spatial environmental gradients and range-size evolution. However, ecologically, these results suggest that Liolaemus might be increasingly threatened if, as predicted by theory, ranges retract and contract continuously under persisting climate warming, potentially increasing extinction risks at high latitudes and elevations.
Comparison of ANFIS Based SSSC, STATCOM and UPFC Controllers for Transient Stability Improvement
Directory of Open Access Journals (Sweden)
Gholamreza Arab Markadeh
2010-12-01
Full Text Available This paper presents the comparative performance of neuro- Fuzzy controlled Voltage Source Converters (VSC based Flexible AC Transmission System (FACTS devices, such as Static Synchronous Series Compensator (SSSC, Static Synchronous Compensator (STATCOM, and Unified Power Flow Controller (UPFC in terms of improvement in transient stability. In neuro-fuzzy control method the simplicity of fuzzy systems and the ability of training in neural networks have been combined. The training data set the parameters of membership functions in fuzzy controller. This Adaptive Network Fuzzy Inference System (ANFIS can track the given input-output data in order to conform to the desired controller. The maximization of energy function of UPFC is used as an objective function to generate the training data. Proposed method is tested on a single machine infinitive bus system to confirm its performance through simulation. The results prove the noticeable influence of ANFIS controlled UPFC on increasing Critical Clearing Time (CCT of system.
King, Gary; Rosen, Ori; Tanner, Martin A.
2004-09-01
This collection of essays brings together a diverse group of scholars to survey the latest strategies for solving ecological inference problems in various fields. The last half-decade has witnessed an explosion of research in ecological inference--the process of trying to infer individual behavior from aggregate data. Although uncertainties and information lost in aggregation make ecological inference one of the most problematic types of research to rely on, these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, by business in marketing research, and by governments in policy analysis.
Directory of Open Access Journals (Sweden)
Chien-Lin Huang
2015-01-01
Full Text Available This study aims to construct a typhoon precipitation forecast model providing forecasts one to six hours in advance using optimal model parameters and structures retrieved from a combination of the adaptive network-based fuzzy inference system (ANFIS and artificial intelligence. To enhance the accuracy of the precipitation forecast, two structures were then used to establish the precipitation forecast model for a specific lead-time: a single-model structure and a dual-model hybrid structure where the forecast models of higher and lower precipitation were integrated. In order to rapidly, automatically, and accurately retrieve the optimal parameters and structures of the ANFIS-based precipitation forecast model, a tabu search was applied to identify the adjacent radius in subtractive clustering when constructing the ANFIS structure. The coupled structure was also employed to establish a precipitation forecast model across short and long lead-times in order to improve the accuracy of long-term precipitation forecasts. The study area is the Shimen Reservoir, and the analyzed period is from 2001 to 2009. Results showed that the optimal initial ANFIS parameters selected by the tabu search, combined with the dual-model hybrid method and the coupled structure, provided the favors in computation efficiency and high-reliability predictions in typhoon precipitation forecasts regarding short to long lead-time forecasting horizons.
Energy Technology Data Exchange (ETDEWEB)
Roberto, Baccoli; Ubaldo, Carlini; Stefano, Mariotti; Roberto, Innamorati; Elisa, Solinas; Paolo, Mura [Institute of Technical Physics of the University of Cagliari, via Marengo 1, 09123 Cagliari (Italy)
2010-06-15
This paper deals with the development of methods for non steady state test of solar thermal collectors. Our goal is to infer performances in steady-state conditions in terms of the efficiency curve when measures in transient conditions are the only ones available. We take into consideration the method of identification of a system in dynamic conditions by applying a Graybox Identification Model and a Dynamic Adaptative Linear Neural Network (ALNN) model. The study targets the solar collector with evacuated pipes, such as Dewar pipes. The mathematical description that supervises the functioning of the solar collector in transient conditions is developed using the equation of the energy balance, with the aim of determining the order and architecture of the two models. The input and output vectors of the two models are constructed, considering the measures of 4 days of solar radiation, flow mass, environment and heat-transfer fluid temperature in the inlet and outlet from the thermal solar collector. The efficiency curves derived from the two models are detected in correspondence to the test and validation points. The two synthetic simulated efficiency curves are compared with the actual efficiency curve certified by the Swiss Institute Solartechnik Puffung Forschung which tested the solar collector performance in steady-state conditions according to the UNI-EN 12975 standard. An acquisition set of measurements of only 4 days in the transient condition was enough to trace through a Graybox State Space Model the efficiency curve of the tested solar thermal collector, with a relative error of synthetic values with respect to efficiency certified by SPF, lower than 0.5%, while with the ALNN model the error is lower than 2.2% with respect to certified one. (author)
Boutalis, Yiannis; Theodoridis, Dimitris C; Christodoulou, Manolis A
2009-04-01
The indirect adaptive regulation of unknown nonlinear dynamical systems is considered in this paper. The method is based on a new neuro-fuzzy dynamical system (neuro-FDS) definition, which uses the concept of adaptive fuzzy systems (AFSs) operating in conjunction with high-order neural network functions (FHONNFs). Since the plant is considered unknown, we first propose its approximation by a special form of an FDS and then the fuzzy rules are approximated by appropriate HONNFs. Thus, the identification scheme leads up to a recurrent high-order neural network (RHONN), which however takes into account the fuzzy output partitions of the initial FDS. The proposed scheme does not require a priori experts' information on the number and type of input variable membership functions making it less vulnerable to initial design assumptions. Once the system is identified around an operation point, it is regulated to zero adaptively. Weight updating laws for the involved HONNFs are provided, which guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. The existence of the control signal is always assured by introducing a novel method of parameter hopping, which is incorporated in the weight updating law. Simulations illustrate the potency of the method and comparisons with conventional approaches on benchmarking systems are given. Also, the applicability of the method is tested on a direct current (dc) motor system where it is shown that by following the proposed procedure one can obtain asymptotic regulation.
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
Institute of Scientific and Technical Information of China (English)
柴园园; 贾利民
2011-01-01
In order to solve the defects of consequent part expression in ANFIS (adaptive neural fuzzy inference system) model and several shortcomings in FIS (fuzzy inference system), this paper presents a Choquet integral-OWA (outlook web access) based FIS, known as AggFIS. This model has advantages in consequent part of fuzzy rule, universal expression of fuzzy inference operator and importance factor of each input and each rule, aiming at establish fuzzy inference system that can fully reflect the essence of fuzzy logic and human thinking patterns. If AggFIS is combined with a feed forward-type neural network according to the basic principles of fuzzy neural network, the Choquet integral-OWA based adaptive neural fuzzy inference system (Agg-ANFIS) is obtained, which is applied to the evaluation of traffic level of service. Experimental results show that Agg-ANFIS is a universal approximates because of its nonlinear mapping capability by training and can be used in, analysis and prediction of complex systems modelling.%针对已有的自适应神经模糊推理系统(ANFIS)在模糊规则后件表达上的缺陷和常见的模糊推理系统存在的主要问题,提出基于Choquet积分OWA的模糊推理系统(AggFIS),在模糊规则的后件表达、模糊算子的普适性和输入及规则的权重等方面有很大优势,它试图建立能够充分体现模糊逻辑本质和人类思维模式的模糊推理系统.根据模糊神经网的基本原理将AggFIS与前馈神经网络相结合,得到基于Choquet积分-OWA的自适应神经模糊推理系统(Agg-ANFIS),并将该模型应用于交通服务水平评价问题.实验结果证明,基于Choquet积分OWA的自适应神经模糊推理系统具有很好的非线性映射功能,它的本质是一类通用逼近器,为解决复杂系统的建模、分析及预测问题提供了有效的途径.
Labarbe, Rudi; Janssens, Guillaume; Sterpin, Edmond
2016-09-01
In proton therapy, quantification of the proton range uncertainty is important to achieve dose distribution compliance. The promising accuracy of prompt gamma imaging (PGI) suggests the development of a mathematical framework using the range measurements to convert population based estimates of uncertainties into patient specific estimates with the purpose of plan adaptation. We present here such framework using Bayesian inference. The sources of uncertainty were modeled by three parameters: setup bias m, random setup precision r and water equivalent path length bias u. The evolution of the expectation values E(m), E(r) and E(u) during the treatment was simulated. The expectation values converged towards the true simulation parameters after 5 and 10 fractions, for E(m) and E(u), respectively. E(r) settle on a constant value slightly lower than the true value after 10 fractions. In conclusion, the simulation showed that there is enough information in the frequency distribution of the range errors measured by PGI to estimate the expectation values and the confidence interval of the model parameters by Bayesian inference. The updated model parameters were used to compute patient specific lateral and local distal margins for adaptive re-planning.
Rohatgi, Vijay K
2003-01-01
Unified treatment of probability and statistics examines and analyzes the relationship between the two fields, exploring inferential issues. Numerous problems, examples, and diagrams--some with solutions--plus clear-cut, highlighted summaries of results. Advanced undergraduate to graduate level. Contents: 1. Introduction. 2. Probability Model. 3. Probability Distributions. 4. Introduction to Statistical Inference. 5. More on Mathematical Expectation. 6. Some Discrete Models. 7. Some Continuous Models. 8. Functions of Random Variables and Random Vectors. 9. Large-Sample Theory. 10. General Meth
Appraisal of soft computing techniques in prediction of total bed material load in tropical rivers
Indian Academy of Sciences (India)
C K Chang; H Md Azamathulla; N A Zakaria; A Ab Ghani
2012-02-01
This paper evaluates the performance of three soft computing techniques, namely Gene-Expression Programming (GEP) (Zakaria et al 2010), Feed Forward Neural Networks (FFNN) (Ab Ghani et al 2011), and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the prediction of total bed material load for three Malaysian rivers namely Kurau, Langat and Muda. The results of present study are very promising: FFNN (2 = 0.958, RMSE = 0.0698), ANFIS (2 = 0.648, RMSE = 6.654), and GEP (2 = 0.97, RMSE = 0.057), which support the use of these intelligent techniques in the prediction of sediment loads in tropical rivers.
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.
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.
Space radiation effect on fibre optical gyroscope control circuit and compensation algorithm
Institute of Scientific and Technical Information of China (English)
Zhang Chun-Xi; Tian Hai-Ting; Li Min; Jin Jing; Song Ning-Fang
2008-01-01
The process of a γ-irradiation experiment of fibre optical gyroscope (FOG) control circuit was described,in which it is demonstrated that the FOG control circuit,except for D/A converter,could endure the dose of 10krad with the protection of cabin material.The distortion and drift in D/A converter due to radiation,which affect the performance of FOG seriously,was indicated based on the elemental analysis.Finally,a compensation network based on adaptive neuro-fuzzy inference system is proposed and its function is verified by simulation.
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.
APPLICATION OF FUZZY ANALYTIC HIERARCHY PROCESS TO BUILDING RESEARCH TEAMS
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Karol DĄBROWSKI
2016-01-01
Full Text Available Building teams has a fundamental impact for execution of research and development projects. The teams appointed for the needs of given projects are based on individuals from both inside and outside of the organization. Knowledge is not only a product available on the market but also an intangible resource affecting their internal and external processes. Thus it is vitally important for businesses and scientific research facilities to effectively manage knowledge within project teams. The article presents a proposal to use Fuzzy AHP (Analytic Hierarchy Process and ANFIS (Adaptive Neuro Fuzzy Inference System methods in working groups building for R&D projects on the basis of employees skills.
FAULT DIAGNOSIS BASED ON INTE- GRATION OF CLUSTER ANALYSIS,ROUGH SET METHOD AND FUZZY NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
Feng Zhipeng; Song Xigeng; Chu Fulei
2004-01-01
In order to increase the efficiency and decrease the cost of machinery diagnosis, a hybrid system of computational intelligence methods is presented. Firstly, the continuous attributes in diagnosis decision system are discretized with the self-organizing map (SOM) neural network. Then, dynamic reducts are computed based on rough set method, and the key conditions for diagnosis are found according to the maximum cluster ratio. Lastly, according to the optimal reduct, the adaptive neuro-fuzzy inference system (ANFIS) is designed for fault identification. The diagnosis of a diesel verifies the feasibility of engineering applications.
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.
ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study.
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.
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.
Artificial Intelligence Techniques for the Estimation of Direct Methanol Fuel Cell Performance
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.
User-Centered Evaluation of Adaptive and Adaptable Systems
Velsen, van Lex; Geest, van der Thea M.; Klaassen, Rob F.
2009-01-01
Adaptive and adaptable systems provide tailored output to various users in various contexts. While adaptive systems base their output on implicit inferences, adaptable systems use explicitly provided information. Since the presentation or output of these systems is adapted, standard user-centered ev
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.
Network inference via adaptive optimal design
Stigter, J.D.; Molenaar, J.
2012-01-01
Background Current research in network reverse engineering for genetic or metabolic networks very often does not include a proper experimental and/or input design. In this paper we address this issue in more detail and suggest a method that includes an iterative design of experiments based, on the m
Adaptive importance sampling for control and inference
Kappen, Hilbert Johan; Ruiz, Hans Christian
2015-01-01
Path integral (PI) control problems are a restricted class of non-linear control problems that can be solved formally as a Feyman-Kac path integral and can be estimated using Monte Carlo sampling. In this contribution we review path integral control theory in the finite horizon case. We subsequently focus on the problem how to compute and represent control solutions. Within the PI theory, the question of how to compute becomes the question of importance sampling. Efficient importance samplers...
Probability biases as Bayesian inference
Directory of Open Access Journals (Sweden)
Andre; C. R. Martins
2006-11-01
Full Text Available In this article, I will show how several observed biases in human probabilistic reasoning can be partially explained as good heuristics for making inferences in an environment where probabilities have uncertainties associated to them. Previous results show that the weight functions and the observed violations of coalescing and stochastic dominance can be understood from a Bayesian point of view. We will review those results and see that Bayesian methods should also be used as part of the explanation behind other known biases. That means that, although the observed errors are still errors under the be understood as adaptations to the solution of real life problems. Heuristics that allow fast evaluations and mimic a Bayesian inference would be an evolutionary advantage, since they would give us an efficient way of making decisions. %XX In that sense, it should be no surprise that humans reason with % probability as it has been observed.
Design and Implementation of Neuro-Fuzzy Controller Using FPGA for Sun Tracking System
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...
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.
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.
User/Tutor Optimal Learning Path in E-Learning Using Comprehensive Neuro-Fuzzy Approach
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…
Boundedly rational learning and heterogeneous trading strategies with hybrid neuro-fuzzy models
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
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.
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.
A mathematical model of neuro-fuzzy approximation in image classification
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.
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.
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.
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
Neuro-fuzzy modeling of multi-field surface neuroprostheses for hand grasp
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...
Edificio project: A neuro-fuzzy approach to building energy management systems
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
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.
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.
Digital modelling of landscape and soil in a mountainous region: A neuro-fuzzy approach
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.
DEFF Research Database (Denmark)
Andersen, Jesper
2009-01-01
Collateral evolution the problem of updating several library-using programs in response to API changes in the used library. In this dissertation we address the issue of understanding collateral evolutions by automatically inferring a high-level specification of the changes evident in a given set ...... specifications inferred by spdiff in Linux are shown. We find that the inferred specifications concisely capture the actual collateral evolution performed in the examples....
Autonomous forward inference via DNA computing
Institute of Scientific and Technical Information of China (English)
Fu Yan; Li Gen; Li Yin; Meng Dazhi
2007-01-01
Recent studies direct the researchers into building DNA computing machines with intelligence, which is measured by three main points: autonomous, programmable and able to learn and adapt. Logical inference plays an important role in programmable information processing or computing. Here we present a new method to perform autonomous molecular forward inference for expert system.A novel repetitive recognition site (RRS) technique is invented to design rule-molecules in knowledge base. The inference engine runs autonomously by digesting the rule-molecule, using a Class ⅡB restriction enzyme PpiⅠ. Concentration model has been built to show the feasibility of the inference process under ideal chemical reaction conditions. Moreover, we extend to implement a triggering communication between molecular automata, as a further application of the RRS technique in our model.
Bayesian Cosmological inference beyond statistical isotropy
Souradeep, Tarun; Das, Santanu; Wandelt, Benjamin
2016-10-01
With advent of rich data sets, computationally challenge of inference in cosmology has relied on stochastic sampling method. First, I review the widely used MCMC approach used to infer cosmological parameters and present a adaptive improved implementation SCoPE developed by our group. Next, I present a general method for Bayesian inference of the underlying covariance structure of random fields on a sphere. We employ the Bipolar Spherical Harmonic (BipoSH) representation of general covariance structure on the sphere. We illustrate the efficacy of the method with a principled approach to assess violation of statistical isotropy (SI) in the sky maps of Cosmic Microwave Background (CMB) fluctuations. The general, principled, approach to a Bayesian inference of the covariance structure in a random field on a sphere presented here has huge potential for application to other many aspects of cosmology and astronomy, as well as, more distant areas of research like geosciences and climate modelling.
Energy Technology Data Exchange (ETDEWEB)
Petrov, S.
1996-10-01
Languages with a solvable implication problem but without complete and consistent systems of inference rules (`poor` languages) are considered. The problem of existence of finite complete and consistent inference rule system for a ``poor`` language is stated independently of the language or rules syntax. Several properties of the problem arc proved. An application of results to the language of join dependencies is given.
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.
Artificial Intelligent Control for a Novel Advanced Microwave Biodiesel Reactor
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.
Efficient ECG signal analysis using wavelet technique for arrhythmia detection: an ANFIS approach
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.
Zhang, Y. S.; Wang, H.; Chen, G. L.; Zhang, X. Q.
2007-03-01
Advanced high strength steels are being increasingly used in the automotive industry to reduce weight and improve fuel economy. However, due to increased physical properties and chemistry of high strength steels, it is difficult to directly substitute these materials into production processes currently designed for mild steels. New process parameters and process-related issues must be developed and understood for high strength steels. Among all issues, endurance of the electrode cap is the most important. In this paper, electrode wear characteristics of hot-dipped galvanized dual-phase (DP600) steels and the effect on weld quality are firstly analysed. An electrode displacement curve which can monitor electrode wear was measured by a developing experimental system using a servo gun. A neuro-fuzzy inference system based on the electrode displacement curve is developed for minimizing the effect of a worn electrode on weld quality by adaptively adjusting input variables based on the measured electrode displacement curve when electrode wear occurs. A modified current curve is implemented to reduce the effects of electrode wear on weld quality using a developed neuro-fuzzy system.
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.
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.
Nagao, Makoto
1990-01-01
Knowledge and Inference discusses an important problem for software systems: How do we treat knowledge and ideas on a computer and how do we use inference to solve problems on a computer? The book talks about the problems of knowledge and inference for the purpose of merging artificial intelligence and library science. The book begins by clarifying the concept of """"knowledge"""" from many points of view, followed by a chapter on the current state of library science and the place of artificial intelligence in library science. Subsequent chapters cover central topics in the artificial intellig
Probability and Statistical Inference
Prosper, Harrison B.
2006-01-01
These lectures introduce key concepts in probability and statistical inference at a level suitable for graduate students in particle physics. Our goal is to paint as vivid a picture as possible of the concepts covered.
Introductory statistical inference
Mukhopadhyay, Nitis
2014-01-01
This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, figures, tables, and computer simulations to develop and illustrate concepts. Drills and boxed summaries emphasize and reinforce important ideas and special techniques.Beginning with a review of the basic concepts and methods in probability theory, moments, and moment generating functions, the author moves to more intricate topics. Introductory Statistical Inference studies multivariate random variables, exponential families of dist
Prediction on carbon dioxide emissions based on fuzzy rules
Pauzi, Herrini; Abdullah, Lazim
2014-06-01
There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.
Fault location in underground cables using ANFIS nets and discrete wavelet transform
Directory of Open Access Journals (Sweden)
Shimaa Barakat
2014-12-01
Full Text Available This paper presents an accurate algorithm for locating faults in a medium voltage underground power cable using a combination of Adaptive Network-Based Fuzzy Inference System (ANFIS and discrete wavelet transform (DWT. The proposed method uses five ANFIS networks and consists of 2 stages, including fault type classification and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., the maximum detailed energy of three phase and zero sequence currents. Other four ANFIS networks are utilized to pinpoint the faults (one for each fault type. Four inputs, i.e., the maximum detailed energy of three phase and zero sequence currents, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on the cable. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances.
Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach
Taufik, Afirah; Sakinah Syed Ahmad, Sharifah
2016-06-01
The aim of this paper is to propose a method to classify the land covers of a satellite image based on fuzzy rule-based system approach. The study uses bands in Landsat 8 and other indices, such as Normalized Difference Water Index (NDWI), Normalized difference built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) as input for the fuzzy inference system. The selected three indices represent our main three classes called water, built- up land, and vegetation. The combination of the original multispectral bands and selected indices provide more information about the image. The parameter selection of fuzzy membership is performed by using a supervised method known as ANFIS (Adaptive neuro fuzzy inference system) training. The fuzzy system is tested for the classification on the land cover image that covers Klang Valley area. The results showed that the fuzzy system approach is effective and can be explored and implemented for other areas of Landsat data.
Parameter inference with estimated covariance matrices
Sellentin, Elena
2015-01-01
When inferring parameters from a Gaussian-distributed data set by computing a likelihood, a covariance matrix is needed that describes the data errors and their correlations. If the covariance matrix is not known a priori, it may be estimated and thereby becomes a random object with some intrinsic uncertainty itself. We show how to infer parameters in the presence of such an estimated covariance matrix, by marginalising over the true covariance matrix, conditioned on its estimated value. This leads to a likelihood function that is no longer Gaussian, but rather an adapted version of a multivariate $t$-distribution, which has the same numerical complexity as the multivariate Gaussian. As expected, marginalisation over the true covariance matrix improves inference when compared with Hartlap et al.'s method, which uses an unbiased estimate of the inverse covariance matrix but still assumes that the likelihood is Gaussian.
Adaptation and complexity in repeated games
DEFF Research Database (Denmark)
Maenner, Eliot Alexander
2008-01-01
The paper presents a learning model for two-player infinitely repeated games. In an inference step players construct minimally complex inferences of strategies based on observed play, and in an adaptation step players choose minimally complex best responses to an inference. When players randomly ...
Hydrograph estimation with fuzzy chain model
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.
A Novel Approach for the Diagnosis of Diabetes and Liver Cancer using ANFIS and Improved KNN
Directory of Open Access Journals (Sweden)
C. Kalaiselvi
2014-07-01
Full Text Available The multi-factorial, chronicle, severe diseases are cancer and diabetes. As a result of abnormal level of glucose in body leads to heart attack, kidney disease, renal failure and cancer. Many studies have been proved that several types of cancer are possible in diabetes patients having a high blood sugar. Many approaches are proposed in the past to diagnose both cancer and diabetes. Even though the existing approaches are efficient one, the classification accuracy is poor. An Enhanced approach is proposed to achieve a higher efficiency and lower complexity. Adaptive neuro fuzzy inference system is used to classify the dataset with the help of adaptive group based KNN. The Pima Indian diabetes dataset are used as input dataset and classified based on the attribute information. The experimental result shows the classification accuracy is better than the existing approaches such FLANN, ANN with FUZZYKNN.
Making Type Inference Practical
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff; Oxhøj, Nicholas; Palsberg, Jens
1992-01-01
We present the implementation of a type inference algorithm for untyped object-oriented programs with inheritance, assignments, and late binding. The algorithm significantly improves our previous one, presented at OOPSLA'91, since it can handle collection classes, such as List, in a useful way. Abo....... Experiments indicate that the implementation type checks as much as 100 lines pr. second. This results in a mature product, on which a number of tools can be based, for example a safety tool, an image compression tool, a code optimization tool, and an annotation tool. This may make type inference for object...
Knuth, Kevin H
2010-01-01
We present a foundation for inference that unites and significantly extends the approaches of Kolmogorov and Cox. Our approach is based on quantifying finite lattices of logical statements in a way that satisfies general lattice symmetries. With other applications in mind, our derivations assume minimal symmetries, relying on neither complementarity nor continuity or differentiability. Each relevant symmetry corresponds to an axiom of quantification, and these axioms are used to derive a unique set of rules governing quantification of the lattice. These rules form the familiar probability calculus. We also derive a unique quantification of divergence and information. Taken together these results form a simple and clear foundation for the quantification of inference.
Energy Technology Data Exchange (ETDEWEB)
Hanson, K.M.; Cunningham, G.S.
1996-04-01
The authors are developing a computer application, called the Bayes Inference Engine, to provide the means to make inferences about models of physical reality within a Bayesian framework. The construction of complex nonlinear models is achieved by a fully object-oriented design. The models are represented by a data-flow diagram that may be manipulated by the analyst through a graphical programming environment. Maximum a posteriori solutions are achieved using a general, gradient-based optimization algorithm. The application incorporates a new technique of estimating and visualizing the uncertainties in specific aspects of the model.
Watson, Jane
2007-01-01
Inference, or decision making, is seen in curriculum documents as the final step in a statistical investigation. For a formal statistical enquiry this may be associated with sophisticated tests involving probability distributions. For young students without the mathematical background to perform such tests, it is still possible to draw informal…
Stochastic processes inference theory
Rao, Malempati M
2014-01-01
This is the revised and enlarged 2nd edition of the authors’ original text, which was intended to be a modest complement to Grenander's fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.
Causal inference in econometrics
Kreinovich, Vladik; Sriboonchitta, Songsak
2016-01-01
This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.
Directory of Open Access Journals (Sweden)
João Paulo Monteiro
2001-12-01
Full Text Available Russell's The Problems of Philosophy tries to establish a new theory of induction, at the same time that Hume is there accused of an irrational/ scepticism about induction". But a careful analysis of the theory of knowledge explicitly acknowledged by Hume reveals that, contrary to the standard interpretation in the XXth century, possibly influenced by Russell, Hume deals exclusively with causal inference (which he never classifies as "causal induction", although now we are entitled to do so, never with inductive inference in general, mainly generalizations about sensible qualities of objects ( whether, e.g., "all crows are black" or not is not among Hume's concerns. Russell's theories are thus only false alternatives to Hume's, in (1912 or in his (1948.
Energy Technology Data Exchange (ETDEWEB)
KENNETH M. HANSON; JANE M. BOOKER
2000-09-08
The authors an uncertainty analysis of data taken using the Rossi technique, in which the horizontal oscilloscope sweep is driven sinusoidally in time ,while the vertical axis follows the signal amplitude. The analysis is done within a Bayesian framework. Complete inferences are obtained by tilting the Markov chain Monte Carlo technique, which produces random samples from the posterior probability distribution expressed in terms of the parameters.
Continuous Integrated Invariant Inference Project
National Aeronautics and Space Administration — The proposed project will develop a new technique for invariant inference and embed this and other current invariant inference and checking techniques in an...
New Inference Rules for Max-SAT
Li, C M; Planes, J; 10.1613/jair.2215
2011-01-01
Exact Max-SAT solvers, compared with SAT solvers, apply little inference at each node of the proof tree. Commonly used SAT inference rules like unit propagation produce a simplified formula that preserves satisfiability but, unfortunately, solving the Max-SAT problem for the simplified formula is not equivalent to solving it for the original formula. In this paper, we define a number of original inference rules that, besides being applied efficiently, transform Max-SAT instances into equivalent Max-SAT instances which are easier to solve. The soundness of the rules, that can be seen as refinements of unit resolution adapted to Max-SAT, are proved in a novel and simple way via an integer programming transformation. With the aim of finding out how powerful the inference rules are in practice, we have developed a new Max-SAT solver, called MaxSatz, which incorporates those rules, and performed an experimental investigation. The results provide empirical evidence that MaxSatz is very competitive, at least, on ran...
The importance of learning when making inferences
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Jorg Rieskamp
2008-03-01
Full Text Available The assumption that people possess a repertoire of strategies to solve the inference problems they face has been made repeatedly. The experimental findings of two previous studies on strategy selection are reexamined from a learning perspective, which argues that people learn to select strategies for making probabilistic inferences. This learning process is modeled with the strategy selection learning (SSL theory, which assumes that people develop subjective expectancies for the strategies they have. They select strategies proportional to their expectancies, which are updated on the basis of experience. For the study by Newell, Weston, and Shanks (2003 it can be shown that people did not anticipate the success of a strategy from the beginning of the experiment. Instead, the behavior observed at the end of the experiment was the result of a learning process that can be described by the SSL theory. For the second study, by Br"oder and Schiffer (2006, the SSL theory is able to provide an explanation for why participants only slowly adapted to new environments in a dynamic inference situation. The reanalysis of the previous studies illustrates the importance of learning for probabilistic inferences.
Probabilistic Inferences in Bayesian Networks
Ding, Jianguo
2010-01-01
This chapter summarizes the popular inferences methods in Bayesian networks. The results demonstrates that the evidence can propagated across the Bayesian networks by any links, whatever it is forward or backward or intercausal style. The belief updating of Bayesian networks can be obtained by various available inference techniques. Theoretically, exact inferences in Bayesian networks is feasible and manageable. However, the computing and inference is NP-hard. That means, in applications, in ...
A novel transmission line protection using DOST and SVM
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M. Jaya Bharata Reddy
2016-06-01
Full Text Available This paper proposes a smart fault detection, classification and location (SFDCL methodology for transmission systems with multi-generators using discrete orthogonal Stockwell transform (DOST. The methodology is based on synchronized current measurements from remote telemetry units (RTUs installed at both ends of the transmission line. The energy coefficients extracted from the transient current signals due to occurrence of different types of faults using DOST are being utilized for real-time fault detection and classification. Support vector machine (SVM has been deployed for locating the fault distance using the extracted coefficients. A comparative study is performed for establishing the superiority of SVM over other popular computational intelligence methods, such as adaptive neuro-fuzzy inference system (ANFIS and artificial neural network (ANN, for more precise and reliable estimation of fault distance. The results corroborate the effectiveness of the suggested SFDCL algorithm for real-time transmission line fault detection, classification and localization.
Directory of Open Access Journals (Sweden)
M. Muthuramalingam
2014-10-01
Full Text Available This study presents compared with Fuzzy Logic Control (FLC and Adaptive Neuro-Fuzzy Inference System (ANFIS Maximum Power Point Tracking (MPPT algorithms, in terms of parameters like tracking speed, power extraction, efficiency and harmonic analysis under various irradiation and cell temperature conditions of Photovoltaic (PV system. The performance of a PV array are affected by temperature and solar irradiation, In fact, in this system, the experimental implementation and the MATLAB based simulations are In this topology, each Cascaded H-Bridge Inverter (CHBI unit is connected to PV module through an Interleaved Soft Switching Boost Converter (ISSBC. It also offers another advantage such as lower ripple current and switching loss compared to the conventional boost converter. The results are evaluated by simulation and experimental implemented on a 150 W PV panel prototype with the microcontroller platform. The simulation and hardware results show that ANFIS algorithm is more efficient than the FLC algorithm.
Prediction of Heart Attack Risk Using GA-ANFIS Expert System Prototype.
Begic Fazlic, Lejla; Avdagic, Aja; Besic, Ingmar
2015-01-01
The aim of this research is to develop a novel GA-ANFIS expert system prototype for classifying heart disease degree of a patient by using heart diseases attributes (features) and diagnoses taken in the real conditions. Thirteen attributes have been used as inputs to classifiers being based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for the first level of fuzzy model optimization. They are used as inputs in Genetic Algorithm (GA) for the second level of fuzzy model optimization within GA-ANFIS system. GA-ANFIS system performs optimization in two steps. Modelling and validating of the novel GA-ANFIS system approach is performed in MATLAB environment. We compared GA-ANFIS and ANFIS results. The proposed GA-ANFIS model with the predicted value technique is more efficient when diagnosis of heart disease is concerned, as well the earlier method we got by ANFIS model.
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Deme A.C
2016-08-01
Full Text Available This study proposes computational intelligence based models for field strength prediction across the tropical metropolitan environment of Abuja, the federal capital territory of Nigeria. The three networks considered were the Multilayer Perceptron Neural Network (MLP-NN, the Radial Basis Function Neural Network (RBF-NN and the Adaptive Neuro-Fuzzy Inference System (ANFIS. Prediction models based on these networks were created, trained and tested for field strength prediction using received power signals recorded at an operating frequency of 900MHz from multiple Base Transceiver Stations (BTS distributed across the city. Results indicate that the RBF-NN and the ANFIS based models gave predictions with Root Mean Squared Errors (RMSE values less than 5dBm. The RBF-NN based predictor gave the highest prediction accuracy based on RMSE value of 4.41dBm, closely followed by the ANFIS model with 4.69dBm
DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware.
Afifi, Firdaus; Anuar, Nor Badrul; Shamshirband, Shahaboddin; Choo, Kim-Kwang Raymond
2016-01-01
To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO).
Prediction of the heat transfer rate of a single layer wire-on-tube type heat exchanger using ANFIS
Energy Technology Data Exchange (ETDEWEB)
Hayati, Mohsen [Electrical Engineering Department, Faculty of Engineering, Razi University, Tagh-E-Bostan, Kermanshah 67149 (Iran); Computational Intelligence Research Center, Razi University, Tagh-E-Bostan, Kermanshah 67149 (Iran); Rezaei, Abbas; Seifi, Majid [Electrical Engineering Department, Faculty of Engineering, Razi University, Tagh-E-Bostan, Kermanshah 67149 (Iran)
2009-12-15
In this paper, we applied an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for prediction of the heat transfer rate of the wire-on-tube type heat exchanger. Limited experimental data was used for training and testing ANFIS configuration with the help of hybrid learning algorithm consisting of backpropagation and least-squares estimation. The predicted values are found to be in good agreement with the actual values from the experiments with mean relative error less than 2.55%. Also, we compared the proposed ANFIS model to an ANN approach. Results show that the ANFIS model has more accuracy in comparison to ANN approach. Therefore, we can use ANFIS model to predict the performances of thermal systems in engineering applications, such as modeling heat exchangers for heat transfer analysis. (author)
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.
Parameters estimation of squirrel-cage induction motors using ANN and ANFIS
Directory of Open Access Journals (Sweden)
Mehdi Ahmadi Jirdehi
2016-03-01
Full Text Available In the transient behavior analysis of a squirrel-cage induction motor, the parameters of the single-cage and double-cage models are studied. These parameters are usually hard to obtain. This paper presents two new methods to predict the induction motor parameters in the single-cage and double-cage models based on artificial neural network (ANN and adaptive neuro-fuzzy inference system (ANFIS. For this purpose, the experimental data (manufacturer data of 20 induction motors with the different power are used. The experimental data are including of the starting torque and current, maximum torque, full load sleep, efficiency, rated active power and reactive power. The obtained results from the proposed ANN and ANFIS models are compared with each other and with the experimental data, which show a good agreement between the predicted values and the experimental data. But the proposed ANFIS model is more accurate than the proposed ANN model.
Directory of Open Access Journals (Sweden)
C. Amritha
2013-12-01
Full Text Available This paper presents a design of MANFIS (MultipleAdaptive Neuro Fuzzy Inference System based sensor FaultDetection and Isolation (FDI scheme for a three interacting tanksystem. Three pairs of dedicated observers are designed toestimate the three states of the system. The observers designedare fuzzy systems whose optimal membership functions and rulebase are determined by neural networks. The difference betweenthe estimated and measured value is called as residuals.Decision functions are determined from the residuals. Thesefunctions are compared to a threshold value, when the value ofthese functions exceed a particular threshold, the presence offault is indicated. The FDI designed is implemented to detectsensor bias, abrupt sensor failure, sensor drift and sensor freezetypes of sensor faults.
Exploiting expert systems in cardiology: a comparative study.
Economou, George-Peter K; Sourla, Efrosini; Stamatopoulou, Konstantina-Maria; Syrimpeis, Vasileios; Sioutas, Spyros; Tsakalidis, Athanasios; Tzimas, Giannis
2015-01-01
An improved Adaptive Neuro-Fuzzy Inference System (ANFIS) in the field of critical cardiovascular diseases is presented. The system stems from an earlier application based only on a Sugeno-type Fuzzy Expert System (FES) with the addition of an Artificial Neural Network (ANN) computational structure. Thus, inherent characteristics of ANNs, along with the human-like knowledge representation of fuzzy systems are integrated. The ANFIS has been utilized into building five different sub-systems, distinctly covering Coronary Disease, Hypertension, Atrial Fibrillation, Heart Failure, and Diabetes, hence aiding doctors of medicine (MDs), guide trainees, and encourage medical experts in their diagnoses centering a wide range of Cardiology. The Fuzzy Rules have been trimmed down and the ANNs have been optimized in order to focus into each particular disease and produce results ready-to-be applied to real-world patients.
Prediction of low back pain with two expert systems.
Sari, Murat; Gulbandilar, Eyyup; Cimbiz, Ali
2012-06-01
Low back pain (LBP) is one of the common problems encountered in medical applications. This paper proposes two expert systems (artificial neural network and adaptive neuro-fuzzy inference system) for the assessment of the LBP level objectively. The skin resistance and visual analog scale (VAS) values have been accepted as the input variables for the developed systems. The results showed that the expert systems behave very similar to real data and that use of the expert systems can be used to successfully diagnose the back pain intensity. The suggested systems were found to be advantageous approaches in addition to existing unbiased approaches. So far as the authors are aware, this is the first attempt of using the two expert systems achieving very good performance in a real application. In light of some of the limitations of this study, we also identify and discuss several areas that need continued investigation.
DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware
Afifi, Firdaus; Anuar, Nor Badrul; Shamshirband, Shahaboddin
2016-01-01
To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO). PMID:27611312
Automation of Large-scale Computer Cluster Monitoring Information Analysis
Magradze, Erekle; Nadal, Jordi; Quadt, Arnulf; Kawamura, Gen; Musheghyan, Haykuhi
2015-12-01
High-throughput computing platforms consist of a complex infrastructure and provide a number of services apt to failures. To mitigate the impact of failures on the quality of the provided services, a constant monitoring and in time reaction is required, which is impossible without automation of the system administration processes. This paper introduces a way of automation of the process of monitoring information analysis to provide the long and short term predictions of the service response time (SRT) for a mass storage and batch systems and to identify the status of a service at a given time. The approach for the SRT predictions is based on Adaptive Neuro Fuzzy Inference System (ANFIS). An evaluation of the approaches is performed on real monitoring data from the WLCG Tier 2 center GoeGrid. Ten fold cross validation results demonstrate high efficiency of both approaches in comparison to known methods.
TEXTURE BASED LAND COVER CLASSIFICATION ALGORITHM USING GABOR WAVELET AND ANFIS CLASSIFIER
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S. Jenicka
2016-05-01
Full Text Available Texture features play a predominant role in land cover classification of remotely sensed images. In this study, for extracting texture features from data intensive remotely sensed image, Gabor wavelet has been used. Gabor wavelet transform filters frequency components of an image through decomposition and produces useful features. For classification of fuzzy land cover patterns in the remotely sensed image, Adaptive Neuro Fuzzy Inference System (ANFIS has been used. The strength of ANFIS classifier is that it combines the merits of fuzzy logic and neural network. Hence in this article, land cover classification of remotely sensed image has been performed using Gabor wavelet and ANFIS classifier. The classification accuracy of the classified image obtained is found to be 92.8%.
Energy Technology Data Exchange (ETDEWEB)
Dehkordi, Behzad Mirzaeian, E-mail: mirzaeian@eng.ui.ac.i [Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Hezar-Jerib St., Postal code 8174673441, Isfahan (Iran, Islamic Republic of); Parsapoor, Amir, E-mail: amirparsapoor@yahoo.co [Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Hezar-Jerib St., Postal code 8174673441, Isfahan (Iran, Islamic Republic of); Moallem, Mehdi, E-mail: moallem@cc.iut.ac.i [Department of Electrical Engineering, Isfahan University of Technology, Isfahan (Iran, Islamic Republic of); Lucas, Caro, E-mail: lucas@ut.ac.i [Centre of Excellence for Control and Intelligent Processing, Electrical and Computer Engineering Faculty, College of Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)
2011-01-15
In this paper, a brain emotional learning based intelligent controller (BELBIC) is developed to control the switched reluctance motor (SRM) speed. Like other intelligent controllers, BELBIC is model free and is suitable to control nonlinear systems. Motor parameter changes, operating point changes, measurement noise, open circuit fault in one phase and asymmetric phases in SRM are also simulated to show the robustness and superior performance of BELBIC. To compare the BELBIC performance with other intelligent controllers, Fuzzy Logic Controller (FLC) is developed. System responses with BELBIC and FLC are compared. Furthermore, by eliminating the position sensor, a method is introduced to estimate the rotor position. This method is based on Adaptive Neuro Fuzzy Inference System (ANFIS). The estimator inputs are four phase flux linkages. Suggested rotor position estimator is simulated in different conditions. Simulation results confirm the accurate rotor position estimation in different loads and speeds.
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.
ANFIS-based approach for predicting sediment transport in clean sewer
Azamathulla, H. Md.; Ab. Ghani, Aminuddin; Fei, Seow Yen
2012-01-01
The necessity of sewers to carry sediment has been recognized for many years. Typically, old sewage systems were designated based on self-cleansing concept where there is no deposition in sewer. These codes were applicable to non-cohesive sediments (typically storm sewers). This study presents adaptive neuro-fuzzy inference system (ANFIS), which is a combination of neural network and fuzzy logic, as an alternative approach to predict the functional relationships of sediment transport in sewer pipe systems. The proposed relationship can be applied to different boundaries with partially full flow. The present ANFIS approach gives satisfactory results (r2 = 0.98 and RMSE = 0.002431) compared to the existing predictor. PMID:22389640
Azamathulla, H Md; Ab Ghani, Aminuddin; Fei, Seow Yen
2012-03-01
The necessity of sewers to carry sediment has been recognized for many years. Typically, old sewage systems were designated based on self-cleansing concept where there is no deposition in sewer. These codes were applicable to non-cohesive sediments (typically storm sewers). This study presents adaptive neuro-fuzzy inference system (ANFIS), which is a combination of neural network and fuzzy logic, as an alternative approach to predict the functional relationships of sediment transport in sewer pipe systems. The proposed relationship can be applied to different boundaries with partially full flow. The present ANFIS approach gives satisfactory results (r(2) = 0.98 and RMSE = 0.002431) compared to the existing predictor.
Mobile robot navigation in unknown static environments using ANFIS controller
Directory of Open Access Journals (Sweden)
Anish Pandey
2016-09-01
Full Text Available Navigation and obstacle avoidance are the most important task for any mobile robots. This article presents the Adaptive Neuro-Fuzzy Inference System (ANFIS controller for mobile robot navigation and obstacle avoidance in the unknown static environments. The different sensors such as ultrasonic range finder sensor and sharp infrared range sensor are used to detect the forward obstacles in the environments. The inputs of the ANFIS controller are obstacle distances obtained from the sensors, and the controller output is a robot steering angle. The primary objective of the present work is to use ANFIS controller to guide the mobile robot in the given environments. Computer simulations are conducted through MATLAB software and implemented in real time by using C/C++ language running Arduino microcontroller based mobile robot. Moreover, the successful experimental results on the actual mobile robot demonstrate the effectiveness and efficiency of the proposed controller.
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.
Energy Technology Data Exchange (ETDEWEB)
Romero, M. A.; Jimenez, J.; Mochon, J.; Formoso, A.; Bueno, F. [Centro Nacional de Investigaciones Metalurgicas CENIM. Madrid (Spain); Menendez, J. L. [ACERALIA. Gijon Asturias (Spain)
2000-07-01
This work describes the development and further validation of a model devoted to blast furnace hot metal temperature forecast, based on Fuzzy logic principles. The model employs as input variables, the control variables of an actual blast furnace: Blast volume, moisture, coal injection, oxygen addition, etc. and it yields as a result the hot metal temperature with a forecast horizon of forty minutes. As far as the variables used to develop the model have been obtained from data supplied by an actual blast furnaces sensors, it is necessary to properly analyse and handle such data. Especial attention was paid to data temporal correlation, fitting by interpolation the different sampling rates. In the training stage of the model the ANFIS (Adaptive Neuro-Fuzzy Inference System) and the Subtractive Clustering algorithms have been used. (Author) 9 refs.
Shamsipour, Majid; Pahlevani, Zahra; Shabani, Mohsen Ostad; Mazahery, Ali
2016-04-01
Understanding of the electromagnetic stirrer (EMS) process parameters-wear relation in nanocomposite is required for further creation of tailored modifications of process in accordance with the demands for various applications. This study depicts the performance of hybrid algorithm for optimization of the parameters in EMS compocasting of nano-TiC-reinforced Al-Si alloys. Adaptive neuro-fuzzy inference system (ANFIS) coupled with particle swarm optimization (PSO) was applied to find the optimum combination of the inputs including mold temperature, mix time, impeller speed, powder temperature, cast temperature and average particle size. The optimized condition was obtained in minimization of objective function. The objective function is calculated by ANFIS and then minimized by PSO. The optimized parameters were used to produce semisolid cast aluminum matrix composites reinforced with nano-TiC particles. The optimized nanocomposites were then studied for their tribological properties.
Vibration Analysis and Time Series Prediction for Wind Turbine Gearbox Prognostics
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Hossam A. Gabbar
2013-01-01
Full Text Available Premature failure of a gearbox in a wind turbine poses a high risk of increasing the operational and maintenance costs and decreasing the profit margins. Prognostics and health management (PHM techniques are widely used to access the current health condition of the gearbox and project it in future to predict premature failures. This paper proposes such techniques for predicting gearbox health condition index extracted from the vibration signals emanating from the gearbox. The progression of the monitoring index is predicted using two different prediction techniques, adaptive neuro-fuzzy inference system (ANFIS and nonlinear autoregressive model with exogenous inputs (NARX. The proposed prediction techniques are evaluated through sun-spot data-set and applied on vibration based health related monitoring index calculated through psychoacoustic phenomenon. A comparison is given for their prediction accuracy. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation, and their progression.
Quej, Victor H.; Almorox, Javier; Arnaldo, Javier A.; Saito, Laurel
2017-03-01
Daily solar radiation is an important variable in many models. In this paper, the accuracy and performance of three soft computing techniques (i.e., adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and support vector machine (SVM) were assessed for predicting daily horizontal global solar radiation from measured meteorological variables in the Yucatán Peninsula, México. Model performance was assessed with statistical indicators such as root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The performance assessment indicates that the SVM technique with requirements of daily maximum and minimum air temperature, extraterrestrial solar radiation and rainfall has better performance than the other techniques and may be a promising alternative to the usual approaches for predicting solar radiation.
Institute of Scientific and Technical Information of China (English)
XU Guo-an; JING Hong-wen; LI Kai-ge; CHEN Kun-fu
2005-01-01
In order to seek the economical, practical and effective method of obtaining the thickness of broken rock zone, an emerging intelligent prediction method with adaptive neuro-fuzzy inference system (ANFIS) was introduced into the thickness prediction. And the software with functions of creating and applying prediction systems was developed on the platform of MATLAB6.5. The software was used to predict the broken rock zone thickness of drifts at Liangbei coal mine, Xinlong Company of Coal Industry in Xuchang city of Henan province. The results show that the predicted values accord well with the in situ measured ones. Thereby the validity of the software is validated and it provides a new approach to obtaining the broken zone thickness.
Directory of Open Access Journals (Sweden)
Kalaivani Lakshmanan
2014-01-01
Full Text Available In this paper, various intelligent controllers such as Fuzzy Logic Controller (FLC and Adaptive Neuro Fuzzy Inference System (ANFIS-based current compensating techniques are employed for minimizing the torque ripples in switched reluctance motor. FLC and ANFIS controllers are tuned using MATLAB Toolbox. For the purpose of comparison, the performance of conventional Proportional-Integral (PI controller is also considered. The statistical parameters like minimum, maximum, mean, standard deviation of total torque, torque ripple coefficient and the settling time of speed response for various controllers are reported. From the simulation results, it is found that both FLC and ANFIS controllers gives better performance than PI controller. Among the intelligent controllers, ANFIS gives outer performance than FLC due to its good learning and generalization capabilities thereby improves the dynamic performance of SRM drives.
Various multistage ensembles for prediction of heating energy consumption
Directory of Open Access Journals (Sweden)
Radisa Jovanovic
2015-04-01
Full Text Available Feedforward neural network models are created for prediction of daily heating energy consumption of a NTNU university campus Gloshaugen using actual measured data for training and testing. Improvement of prediction accuracy is proposed by using neural network ensemble. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as member of an ensemble. Two conventional averaging methods for obtaining ensemble output are applied; simple and weighted. In order to achieve better prediction results, multistage ensemble is investigated. As second level, adaptive neuro-fuzzy inference system with various clustering and membership functions are used to aggregate the selected ensemble members. Feedforward neural network in second stage is also analyzed. It is shown that using ensemble of neural networks can predict heating energy consumption with better accuracy than the best trained single neural network, while the best results are achieved with multistage ensemble.
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.
Prediction of Anthropometric Dimensions Based on Grey Incidence Analysis and ANFIS
Institute of Scientific and Technical Information of China (English)
CONG Shan; CUI Zhi-ying; ZHANG Wei-yuan
2007-01-01
In order to select the efficient input variables of adaptive neuro-fuzzy inference system (ANFIS)during the prediction anthropometric dimensions, grey incidence (GI) analysis, as a mathematic method that ranks the sequence of importance of lots of variables in complicated factors has been applied. According to the prediction accuracy (A) between the predicted values and actual measured values, the ANFISGI model with the parameters selected by using the GI analysis were mote correct and effective than those done by multiple regression model and the medel with input parameters naneleoted. The model prediction accuracy △Regression = 0.804 7< △ ANFISGI =0.9725, which proves the model with few parameters is mate correct and effective than the other methods.
DEFF Research Database (Denmark)
Andersen, Jesper; Lawall, Julia
2010-01-01
A key issue in maintaining Linux device drivers is the need to keep them up to date with respect to evolutions in Linux internal libraries. Currently, there is little tool support for performing and documenting such changes. In this paper we present a tool, spdiff, that identifies common changes...... developers can use it to extract an abstract representation of the set of changes that others have made. Our experiments on recent changes in Linux show that the inferred generic patches are more concise than the corresponding patches found in commits to the Linux source tree while being safe with respect...
Nonparametric statistical inference
Gibbons, Jean Dickinson
2010-01-01
Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente
Directory of Open Access Journals (Sweden)
Kevin H. Knuth
2012-06-01
Full Text Available We present a simple and clear foundation for finite inference that unites and significantly extends the approaches of Kolmogorov and Cox. Our approach is based on quantifying lattices of logical statements in a way that satisfies general lattice symmetries. With other applications such as measure theory in mind, our derivations assume minimal symmetries, relying on neither negation nor continuity nor differentiability. Each relevant symmetry corresponds to an axiom of quantification, and these axioms are used to derive a unique set of quantifying rules that form the familiar probability calculus. We also derive a unique quantification of divergence, entropy and information.
Statistical inferences in phylogeography
DEFF Research Database (Denmark)
Nielsen, Rasmus; Beaumont, Mark A
2009-01-01
In conventional phylogeographic studies, historical demographic processes are elucidated from the geographical distribution of individuals represented on an inferred gene tree. However, the interpretation of gene trees in this context can be difficult as the same demographic/geographical process ...... may also be challenged by computational problems or poor model choice. In this review, we will describe the development of statistical methods in phylogeographic analysis, and discuss some of the challenges facing these methods....... can randomly lead to multiple different genealogies. Likewise, the same gene trees can arise under different demographic models. This problem has led to the emergence of many statistical methods for making phylogeographic inferences. A popular phylogeographic approach based on nested clade analysis...... is challenged by the fact that a certain amount of the interpretation of the data is left to the subjective choices of the user, and it has been argued that the method performs poorly in simulation studies. More rigorous statistical methods based on coalescence theory have been developed. However, these methods...
Moment inference from tomograms
Day-Lewis, F. D.; Chen, Y.; Singha, K.
2007-01-01
Time-lapse geophysical tomography can provide valuable qualitative insights into hydrologic transport phenomena associated with aquifer dynamics, tracer experiments, and engineered remediation. Increasingly, tomograms are used to infer the spatial and/or temporal moments of solute plumes; these moments provide quantitative information about transport processes (e.g., advection, dispersion, and rate-limited mass transfer) and controlling parameters (e.g., permeability, dispersivity, and rate coefficients). The reliability of moments calculated from tomograms is, however, poorly understood because classic approaches to image appraisal (e.g., the model resolution matrix) are not directly applicable to moment inference. Here, we present a semi-analytical approach to construct a moment resolution matrix based on (1) the classic model resolution matrix and (2) image reconstruction from orthogonal moments. Numerical results for radar and electrical-resistivity imaging of solute plumes demonstrate that moment values calculated from tomograms depend strongly on plume location within the tomogram, survey geometry, regularization criteria, and measurement error. Copyright 2007 by the American Geophysical Union.
Conditional statistical inference with multistage testing designs.
Zwitser, Robert J; Maris, Gunter
2015-03-01
In this paper it is demonstrated how statistical inference from multistage test designs can be made based on the conditional likelihood. Special attention is given to parameter estimation, as well as the evaluation of model fit. Two reasons are provided why the fit of simple measurement models is expected to be better in adaptive designs, compared to linear designs: more parameters are available for the same number of observations; and undesirable response behavior, like slipping and guessing, might be avoided owing to a better match between item difficulty and examinee proficiency. The results are illustrated with simulated data, as well as with real data.
On the Inference of Functional Circadian Networks Using Granger Causality.
Pourzanjani, Arya; Herzog, Erik D; Petzold, Linda R
2015-01-01
Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals.
On the Inference of Functional Circadian Networks Using Granger Causality
Pourzanjani, Arya; Herzog, Erik D.; Petzold, Linda R.
2015-01-01
Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals. PMID:26413748
Inferring deterministic causal relations
Daniusis, Povilas; Mooij, Joris; Zscheischler, Jakob; Steudel, Bastian; Zhang, Kun; Schoelkopf, Bernhard
2012-01-01
We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.
Inferring the eccentricity distribution
Hogg, David W; Bovy, Jo
2010-01-01
Standard maximum-likelihood estimators for binary-star and exoplanet eccentricities are biased high, in the sense that the estimated eccentricity tends to be larger than the true eccentricity. As with most non-trivial observables, a simple histogram of estimated eccentricities is not a good estimate of the true eccentricity distribution. Here we develop and test a hierarchical probabilistic method for performing the relevant meta-analysis, that is, inferring the true eccentricity distribution, taking as input the likelihood functions for the individual-star eccentricities, or samplings of the posterior probability distributions for the eccentricities (under a given, uninformative prior). The method is a simple implementation of a hierarchical Bayesian model; it can also be seen as a kind of heteroscedastic deconvolution. It can be applied to any quantity measured with finite precision--other orbital parameters, or indeed any astronomical measurements of any kind, including magnitudes, parallaxes, or photometr...
Bayesian inference in geomagnetism
Backus, George E.
1988-01-01
The inverse problem in empirical geomagnetic modeling is investigated, with critical examination of recently published studies. Particular attention is given to the use of Bayesian inference (BI) to select the damping parameter lambda in the uniqueness portion of the inverse problem. The mathematical bases of BI and stochastic inversion are explored, with consideration of bound-softening problems and resolution in linear Gaussian BI. The problem of estimating the radial magnetic field B(r) at the earth core-mantle boundary from surface and satellite measurements is then analyzed in detail, with specific attention to the selection of lambda in the studies of Gubbins (1983) and Gubbins and Bloxham (1985). It is argued that the selection method is inappropriate and leads to lambda values much larger than those that would result if a reasonable bound on the heat flow at the CMB were assumed.
Inferring attitudes from mindwandering.
Critcher, Clayton R; Gilovich, Thomas
2010-09-01
Self-perception theory posits that people understand their own attitudes and preferences much as they understand others', by interpreting the meaning of their behavior in light of the context in which it occurs. Four studies tested whether people also rely on unobservable "behavior," their mindwandering, when making such inferences. It is proposed here that people rely on the content of their mindwandering to decide whether it reflects boredom with an ongoing task or a reverie's irresistible pull. Having the mind wander to positive events, to concurrent as opposed to past activities, and to many events rather than just one tends to be attributed to boredom and therefore leads to perceived dissatisfaction with an ongoing task. Participants appeared to rely spontaneously on the content of their wandering minds as a cue to their attitudes, but not when an alternative cause for their mindwandering was made salient.
Likelihood based inference for partially observed renewal processes
Lieshout, van M.N.M.
2016-01-01
This paper is concerned with inference for renewal processes on the real line that are observed in a broken interval. For such processes, the classic history-based approach cannot be used. Instead, we adapt tools from sequential spatial point process theory to propose a Monte Carlo maximum likelihoo
Admissibility of logical inference rules
Rybakov, VV
1997-01-01
The aim of this book is to present the fundamental theoretical results concerning inference rules in deductive formal systems. Primary attention is focused on: admissible or permissible inference rules the derivability of the admissible inference rules the structural completeness of logics the bases for admissible and valid inference rules. There is particular emphasis on propositional non-standard logics (primary, superintuitionistic and modal logics) but general logical consequence relations and classical first-order theories are also considered. The book is basically self-contained and
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
Tahmasebi, Pejman; Hezarkhani, Ardeshir
2012-05-01
The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.
Steinberg, Alan N.; Bowman, Christopher L.
2013-05-01
This paper presents concepts and an implementation scheme to improve information exploitation processes and products by adaptive discovery and processing of contextual information. Context is used in data fusion - and in inferencing in general - to provide expectations and to constrain processing. It also is used to infer or refine desired information ("problem variables") on the basis of other available information ("context variables"). Contextual exploitation becomes critical in several classes of inferencing problems in which traditional information sources do not provide sufficient resolution between entity states or when such states are poorly or incompletely modeled. An adaptive evidence-accrual inference method - adapted from developments in target recognition and scene understanding - is presented; whereby context variables are selected on the basis of (a) their utility in refining explicit problem variables, (b) the probability of evaluating these variables to within a given accuracy, given candidate system actions (data collection, mining or processing), and (c) the cost of such actions. The Joint Directors of Laboratories (JDL) Data Fusion Model, with its extension to dual Resource Management functions, has been adapted to accommodate adaptive information exploitation, to include adaptive context exploitation. The interplay of Data Fusion and Resource Management (DF&RM) functionality in exploiting contextual information is illustrated in terms of the dual-node DF&RM architecture. An important advance is in the integration of data mining methods for data search/discovery and for abductive model refinement.
Diergarten, Anna Katharina; Nieding, Gerhild
2015-01-01
Two studies examined inferences drawn about the protagonist's emotional state in movies (Study 1) or audiobooks (Study 2). Children aged 5, 8, and 10 years old and adults took part. Participants saw or heard 20 movie scenes or sections of audiobooks taken or adapted from the TV show Lassie. An online measure of emotional inference was designed…
An Inference Language for Imaging
DEFF Research Database (Denmark)
Pedemonte, Stefano; Catana, Ciprian; Van Leemput, Koen
2014-01-01
We introduce iLang, a language and software framework for probabilistic inference. The iLang framework enables the definition of directed and undirected probabilistic graphical models and the automated synthesis of high performance inference algorithms for imaging applications. The iLang framework...
Causal Inference and Developmental Psychology
Foster, E. Michael
2010-01-01
Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether…
Harik, Georges
2010-01-01
We introduce a framework for representing a variety of interesting problems as inference over the execution of probabilistic model programs. We represent a "solution" to such a problem as a guide program which runs alongside the model program and influences the model program's random choices, leading the model program to sample from a different distribution than from its priors. Ideally the guide program influences the model program to sample from the posteriors given the evidence. We show how the KL- divergence between the true posterior distribution and the distribution induced by the guided model program can be efficiently estimated (up to an additive constant) by sampling multiple executions of the guided model program. In addition, we show how to use the guide program as a proposal distribution in importance sampling to statistically prove lower bounds on the probability of the evidence and on the probability of a hypothesis and the evidence. We can use the quotient of these two bounds as an estimate of ...
Statistical Inference and String Theory
Heckman, Jonathan J
2013-01-01
In this note we expose some surprising connections between string theory and statistical inference. We consider a large collective of agents sweeping out a family of nearby statistical models for an M-dimensional manifold of statistical fitting parameters. When the agents making nearby inferences align along a d-dimensional grid, we find that the pooled probability that the collective reaches a correct inference is the partition function of a non-linear sigma model in d dimensions. Stability under perturbations to the original inference scheme requires the agents of the collective to distribute along two dimensions. Conformal invariance of the sigma model corresponds to the condition of a stable inference scheme, directly leading to the Einstein field equations for classical gravity. By summing over all possible arrangements of the agents in the collective, we reach a string theory. We also use this perspective to quantify how much an observer can hope to learn about the internal geometry of a superstring com...
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
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...
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...
Improving motor imagery classification with a new BCI design using neuro-fuzzy S-dFasArt.
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.
Institute of Scientific and Technical Information of China (English)
吴小娟; 朱新坚; 曹广益; 屠恒勇
2008-01-01
固体氧化物燃料电池(solid oxide fuel cell,SOFC)是21世纪最有生命力的发电技术之一.文章从SOFC实际应用的角度出发,应用改进的自适应神经模糊推理系统(adaptive neural fuzzy inference system,ANFIS)对SOFC建立了负载稳定和负载变化2种情况下的电特性模型.由于数据来源不足,首先根据SOFC的工作原理,运用电化学、流体动力学等学科理论,建立SOFC的数学模型,基于该数学模型获取ANFIS辨识模型的训练和预测数据.仿真结果显示了改进的ANFIS技术对SOFC系统的建模和控制具有一定的实用价值.
Optimization methods for logical inference
Chandru, Vijay
2011-01-01
Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though ""solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs."" Presenting powerful, proven optimization techniques for logic in
Statistical inference via fiducial methods
Salomé, Diemer
1998-01-01
In this thesis the attention is restricted to inductive reasoning using a mathematical probability model. A statistical procedure prescribes, for every theoretically possible set of data, the inference about the unknown of interest. ... Zie: Summary
On principles of inductive inference
Kostecki, Ryszard Paweł
2011-01-01
We propose an intersubjective epistemic approach to foundations of probability theory and statistical inference, based on relative entropy and category theory, and aimed to bypass the mathematical and conceptual problems of existing foundational approaches.
Flexible retrieval: When true inferences produce false memories.
Carpenter, Alexis C; Schacter, Daniel L
2017-03-01
Episodic memory involves flexible retrieval processes that allow us to link together distinct episodes, make novel inferences across overlapping events, and recombine elements of past experiences when imagining future events. However, the same flexible retrieval and recombination processes that underpin these adaptive functions may also leave memory prone to error or distortion, such as source misattributions in which details of one event are mistakenly attributed to another related event. To determine whether the same recombination-related retrieval mechanism supports both successful inference and source memory errors, we developed a modified version of an associative inference paradigm in which participants encoded everyday scenes comprised of people, objects, and other contextual details. These scenes contained overlapping elements (AB, BC) that could later be linked to support novel inferential retrieval regarding elements that had not appeared together previously (AC). Our critical experimental manipulation concerned whether contextual details were probed before or after the associative inference test, thereby allowing us to assess whether (a) false memories increased for successful versus unsuccessful inferences, and (b) any such effects were specific to after compared with before participants received the inference test. In each of 4 experiments that used variants of this paradigm, participants were more susceptible to false memories for contextual details after successful than unsuccessful inferential retrieval, but only when contextual details were probed after the associative inference test. These results suggest that the retrieval-mediated recombination mechanism that underlies associative inference also contributes to source misattributions that result from combining elements of distinct episodes. (PsycINFO Database Record
Type Inference for Guarded Recursive Data Types
Stuckey, Peter J.; Sulzmann, Martin
2005-01-01
We consider type inference for guarded recursive data types (GRDTs) -- a recent generalization of algebraic data types. We reduce type inference for GRDTs to unification under a mixed prefix. Thus, we obtain efficient type inference. Inference is incomplete because the set of type constraints allowed to appear in the type system is only a subset of those type constraints generated by type inference. Hence, inference only succeeds if the program is sufficiently type annotated. We present refin...
Bayesian Inference: with ecological applications
Link, William A.; Barker, Richard J.
2010-01-01
This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.
An Inference Language for Imaging
DEFF Research Database (Denmark)
Pedemonte, Stefano; Catana, Ciprian; Van Leemput, Koen
2014-01-01
We introduce iLang, a language and software framework for probabilistic inference. The iLang framework enables the definition of directed and undirected probabilistic graphical models and the automated synthesis of high performance inference algorithms for imaging applications. The iLang framewor......-accelerated primitives specializes iLang to the spatial data-structures that arise in imaging applications. We illustrate the framework through a challenging application: spatio-temporal tomographic reconstruction with compressive sensing....
Statistical Inference: The Big Picture.
Kass, Robert E
2011-02-01
Statistics has moved beyond the frequentist-Bayesian controversies of the past. Where does this leave our ability to interpret results? I suggest that a philosophy compatible with statistical practice, labelled here statistical pragmatism, serves as a foundation for inference. Statistical pragmatism is inclusive and emphasizes the assumptions that connect statistical models with observed data. I argue that introductory courses often mis-characterize the process of statistical inference and I propose an alternative "big picture" depiction.
Statistical Inference in Graphical Models
2008-06-17
Probabilistic Network Library ( PNL ). While not fully mature, PNL does provide the most commonly-used algorithms for inference and learning with the efficiency...of C++, and also offers interfaces for calling the library from MATLAB and R 1361. Notably, both BNT and PNL provide learning and inference algorithms...mature and has been used for research purposes for several years, it is written in MATLAB and thus is not suitable to be used in real-time settings. PNL
Engineering an adaptive and socially- aware feedback acquisition.
Almaliki, Malik Rajab
2015-01-01
Self-adaptive software systems are characterised by their ability to monitor changes in their dynamic environment and react to these changes when needed. Adaptation is driven by these changes in the internal state of the system and its external environment. Social Adaptation is a kind of adaptation which gives users’ feedback a primary role in shaping adaptation decisions. Social Adaptation infers and employs users’ collective judgement on the alternative behaviours of a system as the main dr...
A parameter-adaptive dynamic programming approach for inferring cophylogenies
DEFF Research Database (Denmark)
Merkle, Daniel; Middendorf, Martin; Wieseke, Nicolas
2010-01-01
. The proposed method utilizes an event-based concept for reconciliation analyses where the possible events are cospeciations, sortings, duplications, and (host) switches. All known event-based approaches so far assign costs to each type of cophylogenetic events in order to find a cost-minimal reconstruction. Co...
Institute of Scientific and Technical Information of China (English)
游友珍; 戴剑勇
2013-01-01
According to the influencing factors of open-pit mine production process and production cost,the mine production cost model is set up with adaptive fuzzy inference sys-tem,then,the parameters of the model are optimized by parallel genetic algorithm,which realize the minimization of production cost of open-pit mine. With the cement raw material mine as an example,it successfully realized the optimization of production cost and techni-cal economic parameters of open-pit mine,which not only effectively reduced the produc-tion cost of mine,but also provided important reference for production cost control of manu-facturing enterprises.%根据露天矿山生产工艺流程及生产成本影响因素，运用自适应模糊推理系统建立矿山生产成本系统模型，应用并行遗传算法优化模型参数，实现露天矿山生产成本最小化。并以水泥原料矿山为例，成功实现了露天矿山生产成本与技术经济参数的优化问题，这不仅有效地降低了矿山生产成本，而且为制造企业生产成本控制提供了重要参考价值。
Intracranial EEG correlates of implicit relational inference within the hippocampus.
Reber, T P; Do Lam, A T A; Axmacher, N; Elger, C E; Helmstaedter, C; Henke, K; Fell, J
2016-01-01
Drawing inferences from past experiences enables adaptive behavior in future situations. Inference has been shown to depend on hippocampal processes. Usually, inference is considered a deliberate and effortful mental act which happens during retrieval, and requires the focus of our awareness. Recent fMRI studies hint at the possibility that some forms of hippocampus-dependent inference can also occur during encoding and possibly also outside of awareness. Here, we sought to further explore the feasibility of hippocampal implicit inference, and specifically address the temporal evolution of implicit inference using intracranial EEG. Presurgical epilepsy patients with hippocampal depth electrodes viewed a sequence of word pairs, and judged the semantic fit between two words in each pair. Some of the word pairs entailed a common word (e.g., "winter-red," "red-cat") such that an indirect relation was established in following word pairs (e.g., "winter-cat"). The behavioral results suggested that drawing inference implicitly from past experience is feasible because indirect relations seemed to foster "fit" judgments while the absence of indirect relations fostered "do not fit" judgments, even though the participants were unaware of the indirect relations. A event-related potential (ERP) difference emerging 400 ms post-stimulus was evident in the hippocampus during encoding, suggesting that indirect relations were already established automatically during encoding of the overlapping word pairs. Further ERP differences emerged later post-stimulus (1,500 ms), were modulated by the participants' responses and were evident during encoding and test. Furthermore, response-locked ERP effects were evident at test. These ERP effects could hence be a correlate of the interaction of implicit memory with decision-making. Together, the data map out a time-course in which the hippocampus automatically integrates memories from discrete but related episodes to implicitly influence future
Locative inferences in medical texts.
Mayer, P S; Bailey, G H; Mayer, R J; Hillis, A; Dvoracek, J E
1987-06-01
Medical research relies on epidemiological studies conducted on a large set of clinical records that have been collected from physicians recording individual patient observations. These clinical records are recorded for the purpose of individual care of the patient with little consideration for their use by a biostatistician interested in studying a disease over a large population. Natural language processing of clinical records for epidemiological studies must deal with temporal, locative, and conceptual issues. This makes text understanding and data extraction of clinical records an excellent area for applied research. While much has been done in making temporal or conceptual inferences in medical texts, parallel work in locative inferences has not been done. This paper examines the locative inferences as well as the integration of temporal, locative, and conceptual issues in the clinical record understanding domain by presenting an application that utilizes two key concepts in its parsing strategy--a knowledge-based parsing strategy and a minimal lexicon.
Sick, the spectroscopic inference crank
Casey, Andrew R
2016-01-01
There exists an inordinate amount of spectral data in both public and private astronomical archives which remain severely under-utilised. The lack of reliable open-source tools for analysing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this Article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick can be used to provide a nearest-neighbour estimate of model parameters, a numerically optimised point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalise on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-di...
Perception, illusions and Bayesian inference.
Nour, Matthew M; Nour, Joseph M
2015-01-01
Descriptive psychopathology makes a distinction between veridical perception and illusory perception. In both cases a perception is tied to a sensory stimulus, but in illusions the perception is of a false object. This article re-examines this distinction in light of new work in theoretical and computational neurobiology, which views all perception as a form of Bayesian statistical inference that combines sensory signals with prior expectations. Bayesian perceptual inference can solve the 'inverse optics' problem of veridical perception and provides a biologically plausible account of a number of illusory phenomena, suggesting that veridical and illusory perceptions are generated by precisely the same inferential mechanisms.
Automatic Inference of DATR Theories
Barg, P
1996-01-01
This paper presents an approach for the automatic acquisition of linguistic knowledge from unstructured data. The acquired knowledge is represented in the lexical knowledge representation language DATR. A set of transformation rules that establish inheritance relationships and a default-inference algorithm make up the basis components of the system. Since the overall approach is not restricted to a special domain, the heuristic inference strategy uses criteria to evaluate the quality of a DATR theory, where different domains may require different criteria. The system is applied to the linguistic learning task of German noun inflection.
Regular inference as vertex coloring
Costa Florêncio, C.; Verwer, S.
2012-01-01
This paper is concerned with the problem of supervised learning of deterministic finite state automata, in the technical sense of identification in the limit from complete data, by finding a minimal DFA consistent with the data (regular inference). We solve this problem by translating it in its enti
Covering, Packing and Logical Inference
1993-10-01
of Operations Research 43 (1993). [34] *Hooker, J. N., Generalized resolution for 0-1 linear inequalities, Annals of Mathematics and A 16 271-286. [35...Hooker, J. N. and C. Fedjki, Branch-and-cut solution of inference prob- lems in propositional logic, Annals of Mathematics and AI 1 (1990) 123-140. [40
Mathematical Programming and Logical Inference
1990-12-01
solution of inference problems in propositional logic, to appear in Annals of Mathematics and Al. (271 Howard, R. A., and J. E. Matheson, Influence...1981). (281 Jeroslow, R., and J. Wang, Solving propositional satisfiability problems, to appear in Annals of Mathematics and Al. [29] Nilsson, N. J
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...
On principles of inductive inference
Kostecki, Ryszard Paweł
2011-01-01
We discuss the mathematical and conceptual problems of main approaches to foundations of probability theory and statistical inference and propose new foundational approach, aimed to improve the mathematical structure of the theory and to bypass the old conceptual problems. In particular, we introduce the intersubjective interpretation of probability, which is designed to deal with the troubles of `subjective' and `objective' bayesian interpretations.
Inference in hybrid Bayesian networks
DEFF Research Database (Denmark)
Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2009-01-01
Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....
Type inference for COBOL systems
Deursen, A. van; Moonen, L.M.F.
1998-01-01
Types are a good starting point for various software reengineering tasks. Unfortunately, programs requiring reengineering most desperately are written in languages without an adequate type system (such as COBOL). To solve this problem, we propose a method of automated type inference for these lang
An Introduction to Causal Inference
2009-11-02
legitimize causal inference, has removed causation from its natural habitat, and distorted its face beyond recognition. This exclusivist attitude is...In contrast, when the mediation problem is approached from an exclusivist potential-outcome viewpoint, void of the structural guidance of Eq. (28
Nonparametric Bayesian inference in biostatistics
Müller, Peter
2015-01-01
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...
Inferring Centrality from Network Snapshots
Shao, Haibin; Mesbahi, Mehran; Li, Dewei; Xi, Yugeng
2017-01-01
The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. We show that the tempo centrality can be used to construct an accurate estimate of both the propagation rate of influence exerted on consensus networks and the Kirchhoff index of the underlying graph. Moreover, the tempo centrality also encodes the disturbance rejection of nodes in a consensus network. Our findings provide an approach to infer the performance of a consensus network from its temporal data. PMID:28098166
Statistical learning and selective inference.
Taylor, Jonathan; Tibshirani, Robert J
2015-06-23
We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.
Network Inference from Grouped Data
Zhao, Yunpeng
2016-01-01
In medical research, economics, and the social sciences data frequently appear as subsets of a set of objects. Over the past century a number of descriptive statistics have been developed to construct network structure from such data. However, these measures lack a generating mechanism that links the inferred network structure to the observed groups. To address this issue, we propose a model-based approach called the Hub Model which assumes that every observed group has a leader and that the leader has brought together the other members of the group. The performance of Hub Models is demonstrated by simulation studies. We apply this model to infer the relationships among Senators serving in the 110th United States Congress, the characters in a famous 18th century Chinese novel, and the distribution of flora in North America.
Statistical inference on residual life
Jeong, Jong-Hyeon
2014-01-01
This is a monograph on the concept of residual life, which is an alternative summary measure of time-to-event data, or survival data. The mean residual life has been used for many years under the name of life expectancy, so it is a natural concept for summarizing survival or reliability data. It is also more interpretable than the popular hazard function, especially for communications between patients and physicians regarding the efficacy of a new drug in the medical field. This book reviews existing statistical methods to infer the residual life distribution. The review and comparison includes existing inference methods for mean and median, or quantile, residual life analysis through medical data examples. The concept of the residual life is also extended to competing risks analysis. The targeted audience includes biostatisticians, graduate students, and PhD (bio)statisticians. Knowledge in survival analysis at an introductory graduate level is advisable prior to reading this book.
Bayesian Inference for Radio Observations
Lochner, Michelle; Zwart, Jonathan T L; Smirnov, Oleg; Bassett, Bruce A; Oozeer, Nadeem; Kunz, Martin
2015-01-01
(Abridged) New telescopes like the Square Kilometre Array (SKA) will push into a new sensitivity regime and expose systematics, such as direction-dependent effects, that could previously be ignored. Current methods for handling such systematics rely on alternating best estimates of instrumental calibration and models of the underlying sky, which can lead to inaccurate uncertainty estimates and biased results because such methods ignore any correlations between parameters. These deconvolution algorithms produce a single image that is assumed to be a true representation of the sky, when in fact it is just one realisation of an infinite ensemble of images compatible with the noise in the data. In contrast, here we report a Bayesian formalism that simultaneously infers both systematics and science. Our technique, Bayesian Inference for Radio Observations (BIRO), determines all parameters directly from the raw data, bypassing image-making entirely, by sampling from the joint posterior probability distribution. Thi...
Bayesian inference on proportional elections.
Directory of Open Access Journals (Sweden)
Gabriel Hideki Vatanabe Brunello
Full Text Available Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.
Causal inference based on counterfactuals
Directory of Open Access Journals (Sweden)
Höfler M
2005-09-01
Full Text Available Abstract Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.
Optimal Inference in Cointegrated Systems
1988-01-01
This paper studies the properties of maximum likelihood estimates of co-integrated systems. Alternative formulations of such models are considered including a new triangular system error correction mechanism. It is shown that full system maximum likelihood brings the problem of inference within the family that is covered by the locally asymptotically mixed normal asymptotic theory provided that all unit roots in the system have been eliminated by specification and data transformation. This re...
Inferring Centrality from Network Snapshots
Haibin Shao; Mehran Mesbahi; Dewei Li; Yugeng Xi
2017-01-01
The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. We show that the tempo centrality can be used to construct an accurate estimate of both the propagati...
Applied statistical inference with MINITAB
Lesik, Sally
2009-01-01
Through clear, step-by-step mathematical calculations, Applied Statistical Inference with MINITAB enables students to gain a solid understanding of how to apply statistical techniques using a statistical software program. It focuses on the concepts of confidence intervals, hypothesis testing, validating model assumptions, and power analysis.Illustrates the techniques and methods using MINITABAfter introducing some common terminology, the author explains how to create simple graphs using MINITAB and how to calculate descriptive statistics using both traditional hand computations and MINITAB. Sh
Security Inference from Noisy Data
2008-04-08
Junk Mail Samples (JMS)” later) is collected from Hotmail using a different method. JMS is collected from email in inboxes that is reported as spam (or...The data consist of side channel traces from attackers: spam email messages received by Hotmail, one of the largest Web mail services. The basic...similar content and determining the senders of these email messages, one can infer the composition of the botnet. This approach can analyze botnets re
On Quantum Statistical Inference, II
Barndorff-Nielsen, O. E.; Gill, R. D.; Jupp, P.E.
2003-01-01
Interest in problems of statistical inference connected to measurements of quantum systems has recently increased substantially, in step with dramatic new developments in experimental techniques for studying small quantum systems. Furthermore, theoretical developments in the theory of quantum measurements have brought the basic mathematical framework for the probability calculations much closer to that of classical probability theory. The present paper reviews this field and proposes and inte...
Small, Dylan S; Joffe, Marshall M; Lynch, Kevin G; Roy, Jason A; Russell Localio, A
2014-09-10
Tom Ten Have made many contributions to causal inference and biostatistics before his untimely death. This paper reviews Tom's contributions and discusses potential related future research directions. We focus on Tom's contributions to longitudinal/repeated measures categorical data analysis and particularly his contributions to causal inference. Tom's work on causal inference was primarily in the areas of estimating the effect of receiving treatment in randomized trials with nonadherence and mediation analysis. A related area to mediation analysis he was working on at the time of his death was posttreatment effect modification with applications to designing adaptive treatment strategies.
An introduction to causal inference.
Pearl, Judea
2010-02-26
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation.
Shear wave prediction using committee fuzzy model constrained by lithofacies, Zagros basin, SW Iran
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.
El: A Program for Ecological Inference
King, Gary
2004-01-01
The program EI provides a method of inferring individual behavior from aggregate data. It implements the statistical procedures, diagnostics, and graphics from the book A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data (King 1997). Ecological inference, as traditionally defined, is the process of using aggregate (i.e., “ecological”) data to infer discrete individual-level relationships of interest when individual- level data are not avai...
EI: A Program for Ecological Inference
Gary King
2004-01-01
The program EI provides a method of inferring individual behavior from aggregate data. It implements the statistical procedures, diagnostics, and graphics from the book A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data (King 1997). Ecological inference, as traditionally defined, is the process of using aggregate (i.e., "ecological") data to infer discrete individual-level relationships of interest when individual-level data are not ava...
The NIRS Analysis Package: noise reduction and statistical inference.
Fekete, Tomer; Rubin, Denis; Carlson, Joshua M; Mujica-Parodi, Lilianne R
2011-01-01
Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain Matlab toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS.
Lower complexity bounds for lifted inference
DEFF Research Database (Denmark)
Jaeger, Manfred
2015-01-01
the feasibility of lifted inference on certain syntactically defined classes of models. Lower complexity bounds that imply some limitations for the feasibility of lifted inference on more expressive model classes were established earlier in Jaeger (2000; Jaeger, M. 2000. On the complexity of inference about...... that under the assumption that NETIME≠ETIME, there is no polynomial lifted inference algorithm for knowledge bases of weighted, quantifier-, and function-free formulas. Further strengthening earlier results, this is also shown to hold for approximate inference and for knowledge bases not containing...
Bell's theorem, inference, and quantum transactions
Garrett, A. J. M.
1990-04-01
Bell's theorem is expounded as an analysis in Bayesian inference. Assuming the result of a spin measurement on a particle is governed by a causal variable internal (hidden, “local”) to the particle, one learns about it by making a spin measurement; thence about the internal variable of a second particle correlated with the first; and from there predicts the probabilistic result of spin measurements on the second particle. Such predictions are violated by experiment: locality/causality fails. The statistical nature of the observations rules out signalling; acausal, superluminal, or otherwise. Quantum mechanics is irrelevant to this reasoning, although its correct predictions of experiment imply that it has a nonlocal/acausal interpretation. Cramer's new transactional interpretation, which incorporates this feature by adapting the Wheeler-Feynman idea of advanced and retarded processes to the quantum laws, is advocated. It leads to an invaluable way of envisaging quantum processes. The usual paradoxes melt before this, and one, the “delayed choice” experiment, is chosen for detailed inspection. Nonlocality implies practical difficulties in influencing hidden variables, which provides a very plausible explanation for why they have not yet been found; from this standpoint, Bell's theorem reinforces arguments in favor of hidden variables.
Logical inferences in discourse analysis
Institute of Scientific and Technical Information of China (English)
刘峰廷
2014-01-01
Cohesion and coherence are two important characteristics of discourses. Halliday and Hasan have pointed out that cohesion is the basis of coherence and coherence is the premise of forming discourse. The commonly used cohesive devices are: preference, ellipsis, substitution, etc. Discourse coherence is mainly manifested in sentences and paragraphs. However, in real discourse analysis environment, traditional methods on cohesion and coherence are not enough. This article talks about the conception of discourse analysis at the beginning. Then, we list some of the traditional cohesive devices and its uses. Following that, we make corpus analysis. Finally, we explore and find a new device in textual analysis:discourse logical inferences.
Universum Inference and Corpus Homogeneity
Vogel, Carl; Lynch, Gerard; Janssen, Jerom
Universum Inference is re-interpreted for assessment of corpus homogeneity in computational stylometry. Recent stylometric research quantifies strength of characterization within dramatic works by assessing the homogeneity of corpora associated with dramatic personas. A methodological advance is suggested to mitigate the potential for the assessment of homogeneity to be achieved by chance. Baseline comparison analysis is constructed for contributions to debates by nonfictional participants: the corpus analyzed consists of transcripts of US Presidential and Vice-Presidential debates from the 2000 election cycle. The corpus is also analyzed in translation to Italian, Spanish and Portuguese. Adding randomized categories makes assessments of homogeneity more conservative.
SICK: THE SPECTROSCOPIC INFERENCE CRANK
Energy Technology Data Exchange (ETDEWEB)
Casey, Andrew R., E-mail: arc@ast.cam.ac.uk [Institute of Astronomy, University of Cambridge, Madingley Road, Cambdridge, CB3 0HA (United Kingdom)
2016-03-15
There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
2013-01-01
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
Perceptual inference and autistic traits
DEFF Research Database (Denmark)
Skewes, Joshua; Jegindø, Else-Marie Elmholdt; Gebauer, Line
2015-01-01
Autistic people are better at perceiving details. Major theories explain this in terms of bottom-up sensory mechanisms, or in terms of top-down cognitive biases. Recently, it has become possible to link these theories within a common framework. This framework assumes that perception is implicit...... neural inference, combining sensory evidence with prior perceptual knowledge. Within this framework, perceptual differences may occur because of enhanced precision in how sensory evidence is represented, or because sensory evidence is weighted much higher than prior perceptual knowledge...
Inferring Network Structure from Cascades
Ghonge, Sushrut
2016-01-01
Many physical, biological and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we solve the dynamics of general cascade processes. We then offer three topological inversion methods to infer the structure of any directed network given a set of cascade arrival times. Our forward and inverse formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for 5 different cascade models.
A Robust Approach For Acoustic Noise Suppression In Speech Using ANFIS
Martinek, Radek; Kelnar, Michal; Vanus, Jan; Bilik, Petr; Zidek, Jan
2015-11-01
The authors of this article deals with the implementation of a combination of techniques of the fuzzy system and artificial intelligence in the application area of non-linear noise and interference suppression. This structure used is called an Adaptive Neuro Fuzzy Inference System (ANFIS). This system finds practical use mainly in audio telephone (mobile) communication in a noisy environment (transport, production halls, sports matches, etc). Experimental methods based on the two-input adaptive noise cancellation concept was clearly outlined. Within the experiments carried out, the authors created, based on the ANFIS structure, a comprehensive system for adaptive suppression of unwanted background interference that occurs in audio communication and degrades the audio signal. The system designed has been tested on real voice signals. This article presents the investigation and comparison amongst three distinct approaches to noise cancellation in speech; they are LMS (least mean squares) and RLS (recursive least squares) adaptive filtering and ANFIS. A careful review of literatures indicated the importance of non-linear adaptive algorithms over linear ones in noise cancellation. It was concluded that the ANFIS approach had the overall best performance as it efficiently cancelled noise even in highly noise-degraded speech. Results were drawn from the successful experimentation, subjective-based tests were used to analyse their comparative performance while objective tests were used to validate them. Implementation of algorithms was experimentally carried out in Matlab to justify the claims and determine their relative performances.
Dopamine, affordance and active inference.
Directory of Open Access Journals (Sweden)
Karl J Friston
2012-01-01
Full Text Available The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order in which cues are presented. These simulations provide a (Bayes-optimal model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level.
Bayesian inference for OPC modeling
Burbine, Andrew; Sturtevant, John; Fryer, David; Smith, Bruce W.
2016-03-01
The use of optical proximity correction (OPC) demands increasingly accurate models of the photolithographic process. Model building and inference techniques in the data science community have seen great strides in the past two decades which make better use of available information. This paper aims to demonstrate the predictive power of Bayesian inference as a method for parameter selection in lithographic models by quantifying the uncertainty associated with model inputs and wafer data. Specifically, the method combines the model builder's prior information about each modelling assumption with the maximization of each observation's likelihood as a Student's t-distributed random variable. Through the use of a Markov chain Monte Carlo (MCMC) algorithm, a model's parameter space is explored to find the most credible parameter values. During parameter exploration, the parameters' posterior distributions are generated by applying Bayes' rule, using a likelihood function and the a priori knowledge supplied. The MCMC algorithm used, an affine invariant ensemble sampler (AIES), is implemented by initializing many walkers which semiindependently explore the space. The convergence of these walkers to global maxima of the likelihood volume determine the parameter values' highest density intervals (HDI) to reveal champion models. We show that this method of parameter selection provides insights into the data that traditional methods do not and outline continued experiments to vet the method.
DEFF Research Database (Denmark)
Petersen, Kjell Yngve; Søndergaard, Karin; Kongshaug, Jesper
2015-01-01
Adaptive Lighting Adaptive lighting is based on a partial automation of the possibilities to adjust the colour tone and brightness levels of light in order to adapt to people’s needs and desires. IT support is key to the technical developments that afford adaptive control systems. The possibilities...... offered by adaptive lighting control are created by the ways that the system components, the network and data flow can be coordinated through software so that the dynamic variations are controlled in ways that meaningfully adapt according to people’s situations and design intentions. This book discusses...... differently into an architectural body. We also examine what might occur when light is dynamic and able to change colour, intensity and direction, and when it is adaptive and can be brought into interaction with its surroundings. In short, what happens to an architectural space when artificial lighting ceases...
Larsson, Jonas; Solomon, Samuel G; Kohn, Adam
2016-07-01
Adaptation has been widely used in functional magnetic imaging (fMRI) studies to infer neuronal response properties in human cortex. fMRI adaptation has been criticized because of the complex relationship between fMRI adaptation effects and the multiple neuronal effects that could underlie them. Many of the longstanding concerns about fMRI adaptation have received empirical support from neurophysiological studies over the last decade. We review these studies here, and also consider neuroimaging studies that have investigated how fMRI adaptation effects are influenced by high-level perceptual processes. The results of these studies further emphasize the need to interpret fMRI adaptation results with caution, but they also provide helpful guidance for more accurate interpretation and better experimental design. In addition, we argue that rather than being used as a proxy for measurements of neuronal stimulus selectivity, fMRI adaptation may be most useful for studying population-level adaptation effects across cortical processing hierarchies.
Artificial intelligent techniques for optimizing water allocation in a reservoir watershed
Chang, Fi-John; Chang, Li-Chiu; Wang, Yu-Chung
2014-05-01
This study proposes a systematical water allocation scheme that integrates system analysis with artificial intelligence techniques for reservoir operation in consideration of the great uncertainty upon hydrometeorology for mitigating droughts impacts on public and irrigation sectors. The AI techniques mainly include a genetic algorithm and adaptive-network based fuzzy inference system (ANFIS). We first derive evaluation diagrams through systematic interactive evaluations on long-term hydrological data to provide a clear simulation perspective of all possible drought conditions tagged with their corresponding water shortages; then search the optimal reservoir operating histogram using genetic algorithm (GA) based on given demands and hydrological conditions that can be recognized as the optimal base of input-output training patterns for modelling; and finally build a suitable water allocation scheme through constructing an adaptive neuro-fuzzy inference system (ANFIS) model with a learning of the mechanism between designed inputs (water discount rates and hydrological conditions) and outputs (two scenarios: simulated and optimized water deficiency levels). The effectiveness of the proposed approach is tested on the operation of the Shihmen Reservoir in northern Taiwan for the first paddy crop in the study area to assess the water allocation mechanism during drought periods. We demonstrate that the proposed water allocation scheme significantly and substantially avails water managers of reliably determining a suitable discount rate on water supply for both irrigation and public sectors, and thus can reduce the drought risk and the compensation amount induced by making restrictions on agricultural use water.
Spontaneous Trait Inferences on Social Media
Utz, Sonja
2016-01-01
The present research investigates whether spontaneous trait inferences occur under conditions characteristic of social media and networking sites: nonextreme, ostensibly self-generated content, simultaneous presentation of multiple cues, and self-paced browsing. We used an established measure of trait inferences (false recognition paradigm) and a direct assessment of impressions. Without being asked to do so, participants spontaneously formed impressions of people whose status updates they saw. Our results suggest that trait inferences occurred from nonextreme self-generated content, which is commonly found in social media updates (Experiment 1) and when nine status updates from different people were presented in parallel (Experiment 2). Although inferences did occur during free browsing, the results suggest that participants did not necessarily associate the traits with the corresponding status update authors (Experiment 3). Overall, the findings suggest that spontaneous trait inferences occur on social media. We discuss implications for online communication and research on spontaneous trait inferences. PMID:28123646
Polynomial Regressions and Nonsense Inference
Directory of Open Access Journals (Sweden)
Daniel Ventosa-Santaulària
2013-11-01
Full Text Available Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis and psychology, just to mention a few examples. In many cases, the data employed to estimate such specifications are time series that may exhibit stochastic nonstationary behavior. We extend Phillips’ results (Phillips, P. Understanding spurious regressions in econometrics. J. Econom. 1986, 33, 311–340. by proving that an inference drawn from polynomial specifications, under stochastic nonstationarity, is misleading unless the variables cointegrate. We use a generalized polynomial specification as a vehicle to study its asymptotic and finite-sample properties. Our results, therefore, lead to a call to be cautious whenever practitioners estimate polynomial regressions.
Bayesian Inference with Optimal Maps
Moselhy, Tarek A El
2011-01-01
We present a new approach to Bayesian inference that entirely avoids Markov chain simulation, by constructing a map that pushes forward the prior measure to the posterior measure. Existence and uniqueness of a suitable measure-preserving map is established by formulating the problem in the context of optimal transport theory. We discuss various means of explicitly parameterizing the map and computing it efficiently through solution of an optimization problem, exploiting gradient information from the forward model when possible. The resulting algorithm overcomes many of the computational bottlenecks associated with Markov chain Monte Carlo. Advantages of a map-based representation of the posterior include analytical expressions for posterior moments and the ability to generate arbitrary numbers of independent posterior samples without additional likelihood evaluations or forward solves. The optimization approach also provides clear convergence criteria for posterior approximation and facilitates model selectio...
Relevance-driven Pragmatic Inferences
Institute of Scientific and Technical Information of China (English)
王瑞彪
2013-01-01
Relevance theory, an inferential approach to pragmatics, claims that the hearer is expected to pick out the input of op-timal relevance from a mass of alternative inputs produced by the speaker in order to interpret the speaker ’s intentions. The de-gree of the relevance of an input can be assessed in terms of cognitive effects and the processing effort. The input of optimal rele-vance is the one yielding the greatest positive cognitive effect and requiring the least processing effort. This paper attempts to as-sess the degrees of the relevance of a mass of alternative inputs produced by an imaginary speaker from the perspective of her cor-responding hearer in terms of cognitive effects and the processing effort with a view to justifying the feasibility of the principle of relevance in pragmatic inferences.
Nor, Igor; Charlat, Sylvain; Engelstadter, Jan; Reuter, Max; Duron, Olivier; Sagot, Marie-France
2010-01-01
We address in this paper a new computational biology problem that aims at understanding a mechanism that could potentially be used to genetically manipulate natural insect populations infected by inherited, intra-cellular parasitic bacteria. In this problem, that we denote by \\textsc{Mod/Resc Parsimony Inference}, we are given a boolean matrix and the goal is to find two other boolean matrices with a minimum number of columns such that an appropriately defined operation on these matrices gives back the input. We show that this is formally equivalent to the \\textsc{Bipartite Biclique Edge Cover} problem and derive some complexity results for our problem using this equivalence. We provide a new, fixed-parameter tractability approach for solving both that slightly improves upon a previously published algorithm for the \\textsc{Bipartite Biclique Edge Cover}. Finally, we present experimental results where we applied some of our techniques to a real-life data set.
Statistical inference for financial engineering
Taniguchi, Masanobu; Ogata, Hiroaki; Taniai, Hiroyuki
2014-01-01
This monograph provides the fundamentals of statistical inference for financial engineering and covers some selected methods suitable for analyzing financial time series data. In order to describe the actual financial data, various stochastic processes, e.g. non-Gaussian linear processes, non-linear processes, long-memory processes, locally stationary processes etc. are introduced and their optimal estimation is considered as well. This book also includes several statistical approaches, e.g., discriminant analysis, the empirical likelihood method, control variate method, quantile regression, realized volatility etc., which have been recently developed and are considered to be powerful tools for analyzing the financial data, establishing a new bridge between time series and financial engineering. This book is well suited as a professional reference book on finance, statistics and statistical financial engineering. Readers are expected to have an undergraduate-level knowledge of statistics.
Forecasting the NOK/USD Exchange Rate with Machine Learning Techniques
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...