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Sample records for artificial neural network-based

  1. Artificial Neural Network Based State Estimators Integrated into Kalmtool

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad

    2012-01-01

    In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation...

  2. Artificial neural network based modelling of internal combustion engine performance

    OpenAIRE

    Boruah, Dibakor; Thakur, Pintu Kumar; Baruah, Dipal

    2016-01-01

    The present study aims to quantify the applicability of artificial neural network as a black-box model for internal combustion engine performance. In consequence, an artificial neural network (ANN) based model for a four cylinder, four stroke internal combustion diesel engine has been developed on the basis of specific input and output factors, which have been taken from experimental readings for different load and engine speed circumstances. The input parameters that have been used to create...

  3. Artificial neural network based approach to transmission lines protection

    International Nuclear Information System (INIS)

    The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection

  4. Artificial Neural Network Based Approach for short load forecasting

    Directory of Open Access Journals (Sweden)

    Mr. Rajesh Deshmukh

    2011-12-01

    Full Text Available Accurate models for electric power load forecasting are essential to the operation and planning of a power utility company. Load forecasting helps electric utility to make important decisions on trading of power, load switching, and infrastructure development. Load forecasts are extremely important for power utilizes ISOs, financial institutions, and other stakeholder of power sector. Short term load forecasting is a essential part of electric power system planning and operation forecasting made for unit commitment and security assessment, which have a direct impact on operational casts and system security. Conventional ANN based load forecasting method deal with 24 hour ahead load forecasting by using forecasted temp. This can lead to high forecasting errors in case of rapid temperature changes. This paper present a neural network based approach for short term load forecasting considering data for training, validation and testing of neural network.

  5. ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

    Institute of Scientific and Technical Information of China (English)

    X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

    2003-01-01

    An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

  6. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  7. Artificial neural network based inverse design method for circular sliding slopes

    Institute of Scientific and Technical Information of China (English)

    丁德馨; 张志军

    2004-01-01

    Current design method for circular sliding slopes is not so reasonable that it often results in slope sliding. As a result, artificial neural network (ANN) is used to establish an artificial neural network based inverse design method for circular sliding slopes. A sample set containing 21 successful circular sliding slopes excavated in the past is used to train the network. A test sample of 3 successful circular sliding slopes excavated in the past is used to test the trained network. The test results show that the ANN based inverse design method is valid and can be applied to the design of circular sliding slopes.

  8. Artificial Neural Network-Based Fault Distance Locator for Double-Circuit Transmission Lines

    Directory of Open Access Journals (Sweden)

    Anamika Jain

    2013-01-01

    Full Text Available This paper analyses two different approaches of fault distance location in a double circuit transmission lines, using artificial neural networks. The single and modular artificial neural networks were developed for determining the fault distance location under varying types of faults in both the circuits. The proposed method uses the voltages and currents signals available at only the local end of the line. The model of the example power system is developed using Matlab/Simulink software. Effects of variations in power system parameters, for example, fault inception angle, CT saturation, source strength, its X/R ratios, fault resistance, fault type and distance to fault have been investigated extensively on the performance of the neural network based protection scheme (for all ten faults in both the circuits. Additionally, the effects of network changes: namely, double circuit operation and single circuit operation, have also been considered. Thus, the present work considers the entire range of possible operating conditions, which has not been reported earlier. The comparative results of single and modular neural network indicate that the modular approach gives correct fault location with better accuracy. It is adaptive to variation in power system parameters, network changes and works successfully under a variety of operating conditions.

  9. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lund, Ole

    2009-01-01

    this binding event. RESULTS: Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data...

  10. Artificial Neural Network-based error compensation procedure for low-cost encoders

    CERN Document Server

    Dhar, V K; Kaul, S K; Koul, R; Dubey, B P

    2009-01-01

    An Artificial Neural Network-based error compensation method is proposed for improving the accuracy of resolver-based 16-bit encoders by compensating for their respective systematic error profiles. The error compensation procedure, for a particular encoder, involves obtaining its error profile by calibrating it on a precision rotary table, training the neural network by using a part of this data and then determining the corrected encoder angle by subtracting the ANN-predicted error from the measured value of the encoder angle. Since it is not guaranteed that all the resolvers will have exactly similar error profiles because of the inherent differences in their construction on a micro scale, the ANN has been trained on one error profile at a time and the corresponding weight file is then used only for compensating the systematic error of this particular encoder. The systematic nature of the error profile for each of the encoders has also been validated by repeated calibration of the encoders over a period of t...

  11. Artificial neural network based pulse shape analysis in cryogenic detectors for rare event searches

    International Nuclear Information System (INIS)

    We present a method based on an Artificial Neural Network for a pulse shape analysis in cryogenic detectors. To train the neural network a huge amount of pulses with known properties are necessary. Therefore, a data-driven simulation used to generate these sets is explained. Furthermore, these simulations allow detailed studies, especially of the cut efficiency and the signal purity of the developed cut. First results are presented and compared with the performance of alternative algorithms.

  12. Artificial neural network based pulse shape analysis in cryogenic detectors for rare event searches

    Energy Technology Data Exchange (ETDEWEB)

    Zoeller, Andreas [Physik Department E15, Technische Universitaet Muenchen, 85748 Garching (Germany); Collaboration: CRESST-Collaboration

    2015-07-01

    We present a method based on an Artificial Neural Network for a pulse shape analysis in cryogenic detectors. To train the neural network a huge amount of pulses with known properties are necessary. Therefore, a data-driven simulation used to generate these sets is explained. Furthermore, these simulations allow detailed studies, especially of the cut efficiency and the signal purity of the developed cut. First results are presented and compared with the performance of alternative algorithms.

  13. Design And Analysis Of Artificial Neural Network Based Controller For Speed Control Of Induction Motor Using D T C

    Directory of Open Access Journals (Sweden)

    Kusuma Gottapu

    2014-04-01

    Full Text Available This paper presents an improved version of direct torque control (DTC based on Artificial Neural Network technique used for flux position estimation and sector selection. This controller mainly reduces the torque and flux ripples. Direct torque control of induction motor drive has quick torque response without complex orientation transformation and inner loop current control. The major problem associated with DTC drive is the high torque ripples. The important point in ANN based DTC is the right selection of voltage vector. This project presents simple structured neural network for flux position estimation and sector selection for induction motor. The Levenberg-Marquardt back propagation technique has been used to train the neural networks. The simple structure network facilitates a short training and processing times. The neural network based controller is found to be a very useful technique to obtain high performance speed control.

  14. Artificial Neural Networks Based Modeling and Control of Continuous Stirred Tank Reactor

    Directory of Open Access Journals (Sweden)

    R. S.M.N. Malar

    2009-01-01

    Full Text Available Continuous Stirred Tank Reactor (CSTR is one of the common reactors in chemical plant. Problem statement: Developing a model incorporating the nonlinear dynamics of the system warrants lot of computation. An efficient control of the product concentration can be achieved only through accurate model. Approach: In this study, attempts were made to alleviate the above mentioned problem using “Artificial Intelligence” (AI techniques. One of the AI techniques namely Artificial Neural Networks (ANN was used to model the CSTR incorporating its non-linear characteristics. Two nonlinear models based control strategies namely internal model control and direct inverse control were designed using the neural networks and applied to the control of isothermal CSTR. Results: The simulation results for the above control schemes with set point tracking were presented. Conclusion: Results indicated that neural networks can learn accurate models and give good non-linear control when model equations are not known.

  15. Artificial Neural Network based Diagnostic Model For Causes of Success and Failures

    OpenAIRE

    Kaur, Bikrampal; Aggarwal, Himanshu

    2010-01-01

    In this paper an attempt has been made to identify most important human resource factors and propose a diagnostic model based on the back-propagation and connectionist model approaches of artificial neural network (ANN). The focus of the study is on the mobile -communication industry of India. The ANN based approach is particularly important because conventional approaches (such as algorithmic) to the problem solving have their inherent disadvantages. The algorithmic approach is well-suited t...

  16. Rule Extraction from Trained Artificial Neural Network Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Wen-jian; ZHANG Li-xia

    2002-01-01

    This paper discusses how to extract symbolic rules from trained artificial neural network (ANN) in domains involving classification using genetic algorithms (GA). Previous methods based on an exhaustive analysis of network connections and output values have already been demonstrated to be intractable in that the scale-up factor increases with the number of nodes and connections in the network.Some experiments explaining effectiveness of the presented method are given as well.

  17. ARTIFICIAL NEURAL NETWORK BASED DISCRIMINATION OF MINELIKE OBJECTS IN INFRARED IMAGES

    Directory of Open Access Journals (Sweden)

    G.Suganthi

    2014-09-01

    Full Text Available An artificial neural network (ANN model with a simple architecture containing a single hidden layer is presented to discriminate the landmine objects from the acquired infrared images. The proposed method consists of preprocessing, segmentation, feature extraction and ANN based classification. Texture features based on gray level co-occurrence matrix (GLCM are considered as inputs to the neural network classifier. The proposed method is tested on the infrared images acquired from two different soil types namely black cotton soil and Maharashtra sand. The ability of the back propagation neural network in discriminating the landmines from the clutters in the infrared images acquired from inhomogeneous soil is discussed. The results of the field experiments carried out at the outdoor land mine detection test facility, DRDO, Pune are presented. The results are encouraging.

  18. Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria)

    Science.gov (United States)

    Laidi, Maamar; Hanini, Salah; Rezrazi, Ahmed; Yaiche, Mohamed Redha; El Hadj, Abdallah Abdallah; Chellali, Farouk

    2016-01-01

    In this study, a backpropagation artificial neural network (BP-ANN) model is used as an alternative approach to predict solar radiation on tilted surfaces (SRT) using a number of variables involved in physical process. These variables are namely the latitude of the site, mean temperature and relative humidity, Linke turbidity factor and Angstrom coefficient, extraterrestrial solar radiation, solar radiation data measured on horizontal surfaces (SRH), and solar zenith angle. Experimental solar radiation data from 13 stations spread all over Algeria around the year (2004) were used for training/validation and testing the artificial neural networks (ANNs), and one station was used to make the interpolation of the designed ANN. The ANN model was trained, validated, and tested using 60, 20, and 20 % of all data, respectively. The configuration 8-35-1 (8 inputs, 35 hidden, and 1 output neurons) presented an excellent agreement between the prediction and the experimental data during the test stage with determination coefficient of 0.99 and root meat squared error of 5.75 Wh/m2, considering a three-layer feedforward backpropagation neural network with Levenberg-Marquardt training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. This novel model could be used by researchers or scientists to design high-efficiency solar devices that are usually tilted at an optimum angle to increase the solar incident on the surface.

  19. Artificial neural network based fault identification scheme implementation for a three-phase induction motor.

    Science.gov (United States)

    Kolla, Sri R; Altman, Shawn D

    2007-04-01

    This paper presents results from the implementation and testing of a PC based monitoring and fault identification scheme for a three-phase induction motor using artificial neural networks (ANNs). To accomplish the task, a hardware system is designed and built to acquire three-phase voltages and currents from a 1/3 HP squirrel-cage, three-phase induction motor. A software program is written to read the voltages and currents, which are first used to train a feed-forward neural network structure using the JavaNNS program. The trained network is placed in a LabVIEW based program formula node that monitors the voltages and currents online and displays the fault conditions and turns the motor off. The complete system is successfully tested in real time by creating different faults on the motor.

  20. Early detection of system malfunctions in NPPs using artificial neural network based classification

    International Nuclear Information System (INIS)

    Artificial neural networks seem to be a promising tool to perform classification. To do it, the network is trained in a proper way, i.e. both normal and abnormal signals are shown to the network. Using a large enough sample set, after the training period the network is able to distinguish normal and abnormal signals. When a new signal is shown, a network assigns it to either normal or abnormal class, using the information inherently extracted from the sample set. The method is under development but early and promising results indicate the applicability of the tool in real situations. (author)

  1. Artificial Neural Network based Diagnostic Model For Causes of Success and Failures

    CERN Document Server

    Kaur, Bikrampal

    2010-01-01

    In this paper an attempt has been made to identify most important human resource factors and propose a diagnostic model based on the back-propagation and connectionist model approaches of artificial neural network (ANN). The focus of the study is on the mobile -communication industry of India. The ANN based approach is particularly important because conventional approaches (such as algorithmic) to the problem solving have their inherent disadvantages. The algorithmic approach is well-suited to the problems that are well-understood and known solution(s). On the other hand the ANNs have learning by example and processing capabilities similar to that of a human brain. ANN has been followed due to its inherent advantage over conversion algorithmic like approaches and having capabilities, training and human like intuitive decision making capabilities. Therefore, this ANN based approach is likely to help researchers and organizations to reach a better solution to the problem of managing the human resource. The stud...

  2. Long Range Forecast on South West Monsoon Rainfall using Artificial Neural Networks based on Clustering Approach

    Directory of Open Access Journals (Sweden)

    Maya L. Pai

    2014-06-01

    Full Text Available The purpose of this study is to forecast Southwest Indian Monsoon rainfall based on sea surface temperature, sea level pressure, humidity and zonal (u and meridional (v winds. With the aforementioned parameters given as input to an Artificial Neural Network (ANN, the rainfall within 10x10 grids of southwest Indian regions is predicted by means of one of the most efficient clustering methods, namely the Kohonen Self-Organizing Maps (SOM. The ANN is trained with input parameters spanning for 36 years (1960-1995 and tested and validated for a period of 9 years (1996-2004. It is further used to predict the rainfall for 6 years (2005-2010. The results show reasonably good accuracy for the summer monsoon periods June, July, August and September (JJAS of the validation years.

  3. All Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron.

    Science.gov (United States)

    Zhang, Deming; Zeng, Lang; Cao, Kaihua; Wang, Mengxing; Peng, Shouzhong; Zhang, Yue; Zhang, Youguang; Klein, Jacques-Olivier; Wang, Yu; Zhao, Weisheng

    2016-08-01

    Artificial synaptic devices implemented by emerging post-CMOS non-volatile memory technologies such as Resistive RAM (RRAM) have made great progress recently. However, it is still a big challenge to fabricate stable and controllable multilevel RRAM. Benefitting from the control of electron spin instead of electron charge, spintronic devices, e.g., magnetic tunnel junction (MTJ) as a binary device, have been explored for neuromorphic computing with low power dissipation. In this paper, a compound spintronic device consisting of multiple vertically stacked MTJs is proposed to jointly behave as a synaptic device, termed as compound spintronic synapse (CSS). Based on our theoretical and experimental work, it has been demonstrated that the proposed compound spintronic device can achieve designable and stable multiple resistance states by interfacial and materials engineering of its components. Additionally, a compound spintronic neuron (CSN) circuit based on the proposed compound spintronic device is presented, enabling a multi-step transfer function. Then, an All Spin Artificial Neural Network (ASANN) is constructed with the CSS and CSN circuit. By conducting system-level simulations on the MNIST database for handwritten digital recognition, the performance of such ASANN has been investigated. Moreover, the impact of the resolution of both the CSS and CSN and device variation on the system performance are discussed in this work. PMID:27214913

  4. Artificial Neural Network Based Rotor Capacitive Reactance Control for Energy Efficient Wound Rotor Induction Motor

    Directory of Open Access Journals (Sweden)

    K. Siva Kumar

    2012-01-01

    Full Text Available Problem statement: The Rotor reactance control by inclusion of external capacitance in the rotor circuit has been in recent research for improving the performances of Wound Rotor Induction Motor (WRIM. The rotor capacitive reactance is adjusted such that for any desired load torque the efficiency of the WRIM is maximized. The rotor external capacitance can be controlled using a dynamic capacitor in which the duty ratio is varied for emulating the capacitance value. This study presents a novel technique for tracking maximum efficiency point in the entire operating range of WRIM using Artificial Neural Network (ANN. The data for ANN training were obtained on a three phase WRIM with dynamic capacitor control and rotor short circuit at different speed and load torque values. Approach: A novel neural network model based on the back-propagation algorithm has been developed and trained in determining the maximum efficiency of the motor with no prior knowledge of the machine parameters. The input variables to the ANN are stator current (Is, Speed (N and Torque (Tm and the output variable is the duty ratio (D. Results: The target is pre-set and the accuracy of the ANN model is measured using Mean Square Error (MSE and R2 parameters. The result of R2 value of the proposed ANN model is found to be 0.99980. Conclusion: The optimal duty ratio and corresponding optimal rotor capacitance for improving the performances of the motor are predicted for low, medium and full loads by using proposed ANN model.

  5. Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge

    Directory of Open Access Journals (Sweden)

    M. Mohan Raju

    2011-01-01

    Full Text Available The present study demonstrates the application of artificial neural networks (ANNs in predicting the weekly spring discharge. The study was based on the weekly spring discharge from a spring located near Ranichauri in Tehri Garhwal district of Uttarakhand, India. Five models were developed for predicting the spring discharge based on a weekly interval using rainfall, evaporation, temperature with a specified lag time. All models were developed both with one and two hidden layers. Each model was developed with many trials by selecting different network architectures and different number of hidden neurons; finally a best predicting model presented against each developed model. The models were trained with three different algorithms, that is, quick-propagation algorithm, batch backpropagation algorithm, and Levenberg-Marquardt algorithm using weekly data from 1999 to 2005. A best model for the simulation was selected from the three presented algorithms using the statistical criteria such as correlation coefficient (, determination coefficient, or Nash Sutcliff's efficiency (DC. Finally, optimized number of neurons were considered for the best model. Training and testing results revealed that the models were predicting the weekly spring discharge satisfactorily. Based on these criteria, ANN-based model results in better agreement for the computation of spring discharge. LMR models were also developed in the study, and they also gave good results, but, when compared with the ANN methodology, ANN resulted in better optimized values.

  6. Artificial Neural Network Based Method to Mitigate Temporary Over-voltages

    Directory of Open Access Journals (Sweden)

    Iman Sadeghkhani

    2011-09-01

    Full Text Available Uncontrolled energization of large power transformers may result in magnetizing inrush current of high amplitude and switching over-voltages. The most effective method for the limitation of the switching over-voltages is controlled switching since the magnitudes of the produced transients are strongly dependent on the closing instants of the switch.‎ We introduce a harmonic index that its minimum value is corresponding to the best-case switching time.‎ Also, this paper ‎presents an Artificial Neural Network (ANN-based approach to ‎estimate the optimum switching instants for real time applications. In the proposed ANN, second order Levenberg–Marquardt ‎ method is used to train the multilayer perceptron. ANN training is performed based on equivalent circuit parameters of the network. Thus, trained ANN is applicable to every studied system. To verify the effectiveness of the proposed index and accuracy of the ANN-based approach, two case studies are presented and demonstrated.

  7. Abstract Computation in Schizophrenia Detection through Artificial Neural Network Based Systems

    Directory of Open Access Journals (Sweden)

    L. Cardoso

    2015-01-01

    Full Text Available Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason of defective information.

  8. Artificial neural network-based predictive emission monitoring system for NOx emissions

    Energy Technology Data Exchange (ETDEWEB)

    Ciccone, A.; Cinnamon, C.; Niejadlik, P.R. [TransCanada Energy Ltd., Toronto, ON (Canada)]|[Golder Associates, Toronto, ON (Canada)

    2005-07-01

    Considering the nature of long term power supply contracts that do not include mechanisms for cost recovery, developing cost-effective ways to handle changes in legislation impacting on facilities already in operation is extremely important. Also of importance is the age of the facilities, since continuous emissions monitoring (CEM) systems were not required when they were originally put into operation, but they are not yet old enough for capital stock turnover to allow for equipment changes or transition to new operations. An alternative monitoring method that is less expensive and as accurate as traditional (CEM) systems is discussed. TransCanada Energy Ltd., developed a predictive emission monitoring (PEM) system that achieved the required accuracy of the regulatory authorities using four of its gas turbine power plant facilities. Using the power plant operation variables to predict the nitric oxide (NO) portion of the exhaust emissions, the systems are founded on an artificial neural network (ANN). This paper provides a summary of the PEM system architecture and provides background information on the facilities used in the development of this approach. It was concluded that the PEM system provides a cost effective method to monitor emissions accurately and reliably at low emitting natural gas fired facilities. As well, there is a great potential for the system to be used by other industries to monitor and report emissions. The PEM system is an ideal system for the low emitting natural gas fired generating plants however the system could be adapted for other types of industries. 5 refs., 5 tabs., 2 figs.

  9. Development of an artificial neural network-based software for prediction of power plant canal water discharge temperature

    Energy Technology Data Exchange (ETDEWEB)

    Romero, C.E.; Shan, J.F. [Lehigh University, Bethlehem, PA (United States). Energy Research Center

    2005-11-01

    Power plant cooling water systems that interact with nearby effluents are complex non-linear, large-time-delay systems. A neural network-based software tool was developed for prediction of the canal water discharge temperature at a coal-fired power plant as a function of plant operating parameters and local weather conditions, including tide information. The plant has four units totaling an installed capacity of 1550 MW and its water thermal discharge is environmentally regulated. In the summer months, when the price of electricity is very profitable and the risk of exceeding the canal temperature limit is greater, the tradeoff between maximum generation and environmental compliance violations is financially significant. The software is a predictive tool to assist in scheduling load generation among the plant's four units without exceeding a thermal discharge limit of 95{sup o}F. Back propagation neural network architectures were trained using plant operating data with an 'off-set' component. The artificial intelligence models produced reasonable trends for year-round prediction and different operational scenarios. Comparison of measured and predicted canal temperatures indicated an accuracy of less than 0.3{sup o}F over the range between 90 and 95{sup o}F. The software tool was developed as an Object Linking and Embedding (OLE) for Process Control (OPC) client, with real-time communication and interface with the plant Distributed Control System (DCS).

  10. Improvement of Power System Stability using Artificial Neural Network based HVDC Controls

    Directory of Open Access Journals (Sweden)

    Nagu Bhookya

    2013-06-01

    Full Text Available In this paper, investigation is carried out for the improvement of power system stability by utilizing auxiliary controls for controlling HVDC power flow. The current controller model and the line dynamics are considered in the stability analysis. Transient stability analysis is done on a multi-machine system, where, a neural network controller is developed to improve the stability of the power system and to improve the response time of the controller to the changing conditions in power system. The results show the application of the neural network controller in AC-DC power systems.

  11. An Artificial Neural Networks-Based on-Line Monitoring Odor Sensing System

    Directory of Open Access Journals (Sweden)

    Yousif A. Bastaki

    2009-01-01

    Full Text Available Problem statement: There have been many works for odor recognition using different sensor arrays and pattern recognition techniques in last decades. Approach: Although an odor is usually recorded utilizing language expression, it is too difficult for laymen to associate actual odor with that expression. Results: The odor sensing system should be extended to new areas since its standard style where the output pattern from multiple sensors with partially overlapped specificity is recognized by a neural network or multivariate analysis. Conclusion/Recommendations: In this study, we have developed odor sensing system with the capability of the discrimination among closely similar 20 different odor patterns and proposed an on-line classification method using a handheld odor meter (OMX-GR sensor and neural network.

  12. Comparative Study of Reverse Algorithms via Artificial Neural Networks Based on Simulated Indentation Tests

    Institute of Scientific and Technical Information of China (English)

    Somsak Swaddiwudhipong; Edy Harsono; Liu Zishun

    2008-01-01

    The advances in the instrumented indentation equipments and the need to assess the properties of materials of small volume such as those constitute the micro-electro-mechanical devices, micro-electronic packages, and thin films have propelled the interest in material characterization via indentation tests. The load-displacement curves and their charactedstics, namely, the curvature of the loading path, C, and the ra-tio of the remaining and total work done, WR/WT, can be conveniently obtained from finite element simula-tions for various elasto-plastic material properties. The paper reports the comparative study on two reverse neural networks algorithms involving several combinations of databases established from the results ob-tained from simulated indentation tests. The performance of each set of results is analyzed and the most appropriate algodthm identified and reported. The approach with the selected neural networks model has great potential in practical applications on the characterization of a small volume of materials.

  13. Improvement of Power System Stability using Artificial Neural Network based HVDC Controls

    OpenAIRE

    Nagu Bhookya

    2013-01-01

    In this paper, investigation is carried out for the improvement of power system stability by utilizing auxiliary controls for controlling HVDC power flow. The current controller model and the line dynamics are considered in the stability analysis. Transient stability analysis is done on a multi-machine system, where, a neural network controller is developed to improve the stability of the power system and to improve the response time of the controller to the changing conditions in power syste...

  14. An artificial neural network based fast radiative transfer model for simulating infrared sounder radiances

    Indian Academy of Sciences (India)

    Praveen Krishnan; K Srinivasa Ramanujam; C Balaji

    2012-08-01

    The first step in developing any algorithm to retrieve the atmospheric temperature and humidity parameters at various pressure levels is the simulation of the top of the atmosphere radiances that can be measured by the satellite. This study reports the results of radiative transfer simulations for the multichannel infrared sounder of the proposed Indian satellite INSAT-3D due to be launched shortly. Here, the widely used community software k Compressed Atmospheric Radiative Transfer Algorithm (kCARTA) is employed for performing the radiative transfer simulations. Though well established and benchmarked, kCARTA is a line-by-line solver and hence takes enormous computational time and effort for simulating the multispectral radiances for a given atmospheric scene. This necessitates the development of a much faster and at the same time, equally accurate RT model that can drive a real-time retrieval algorithm. In the present study, a fast radiative transfer model using neural networks is proposed to simulate radiances corresponding to the wavenumbers of INSAT-3D. Realistic atmospheric temperature and humidity profiles have been used for training the network. Spectral response functions of GOES-13, a satellite similar in construction, purpose and design and already in use are used. The fast RT model is able to simulate the radiances for 1200 profiles in 18 ms for a 15-channel GOES profile, with a correlation coefficient of over 99%. Finally, the robustness of the model is tested using additional synthetic profiles generated using empirical orthogonal functions (EOF).

  15. Artificial Neural Network based gamma-hadron segregation methodology for TACTIC telescope

    CERN Document Server

    Dhar, V K; Koul, M K; Koul, R; Dubey, B P; Rannot, R C; Yadav, K K; Chandra, P; Kothari, M; Chanchalani, K; Venugopal, K

    2013-01-01

    The sensitivity of a Cherenkov imaging telescope is strongly dependent on the rejection of the cosmic-ray background events. The methods which have been used to achieve the segregation between the gamma-rays from the source and the background cosmic-rays, include methods like Supercuts/Dynamic Supercuts, Maximum likelihood classifier, Kernel methods, Fractals, Wavelets and random forest. While the segregation potential of the neural network classifier has been investigated in the past with modest results, the main purpose of this paper is to study the gamma / hadron segregation potential of various ANN algorithms, some of which are supposed to be more powerful in terms of better convergence and lower error compared to the commonly used Backpropagation algorithm. The results obtained suggest that Levenberg-Marquardt method outperforms all other methods in the ANN domain. Applying this ANN algorithm to $\\sim$ 101.44 h of Crab Nebula data collected by the TACTIC telescope, during Nov. 10, 2005 - Jan. 30, 2006, y...

  16. Artificial Neural Network based γ-hadron segregation methodology for TACTIC telescope

    Energy Technology Data Exchange (ETDEWEB)

    Dhar, V.K., E-mail: veer@barc.gov.in [Astrophysical Sciences Division, Bhabha Atomic Research Centre, Mumbai 400 085 (India); Tickoo, A.K.; Koul, M.K.; Koul, R. [Astrophysical Sciences Division, Bhabha Atomic Research Centre, Mumbai 400 085 (India); Dubey, B.P. [Electronics and Instrumentation Services Division, Bhabha Atomic Research Centre, Mumbai 400 085 (India); Rannot, R.C.; Yadav, K.K.; Chandra, P.; Kothari, M.; Chanchalani, K.; Venugopal, K. [Astrophysical Sciences Division, Bhabha Atomic Research Centre, Mumbai 400 085 (India)

    2013-04-21

    The sensitivity of a Cherenkov imaging telescope is strongly dependent on the rejection of the cosmic-ray background events. The methods which have been used to achieve the segregation between the γ-rays from the source and the background cosmic-rays, include methods like Supercuts/Dynamic Supercuts, Maximum likelihood classifier, Kernel methods, Fractals, Wavelets and random forest. While the segregation potential of the neural network classifier has been investigated in the past with modest results, the main purpose of this paper is to study the γ/hadron segregation potential of various ANN algorithms, some of which are supposed to be more powerful in terms of better convergence and lower error compared to the commonly used Backpropagation algorithm. The results obtained suggest that Levenberg–Marquardt method outperforms all other methods in the ANN domain. Applying this ANN algorithm to ∼101.44h of Crab Nebula data collected by the TACTIC telescope, during November 10, 2005–January 30, 2006, yields an excess of ∼(1141±106) with a statistical significance of ∼11.07σ, as against an excess of ∼(928±100) with a statistical significance of ∼9.40σ obtained with Dynamic Supercuts selection methodology. The main advantage accruing from the ANN methodology is that it is more effective at higher energies and this has allowed us to re-determine the Crab Nebula energy spectrum in the energy range ∼1–24TeV.

  17. Design and Development of Artificial Neural Network Based Tamil Unicode Symbols Identification System

    Directory of Open Access Journals (Sweden)

    Karthick Anand Babu

    2012-01-01

    Full Text Available Design and Development of Unicode and its recognition especially for Indian script is an active area of research today. An attempt is made to identify Tamil- a vernacular of southern India, which is also the official language of Tamilnadu. Tamil language present great challenges to an OCR designer due to the large number (247 letters in the alphabet, the sophisticated ways in which they combine, and the complicated graphemes they result in. The conventional programming methods of mapping symbol images into matrices, analyzing pixel and/or vector data and trying to decide which symbol corresponds to which character would yield little or no realistic results. Clearly the needed methodology will be one that can detect closeness of graphic representations to known symbols based on the character height, character width, the number of horizontal lines (long and short, the number of vertical lines (long and short, number of slope lines, special dots and based on that the glyphs are now set ready for classification. The extracted features are passed to neural network where the characters are classified by supervised learning of Back Propagation algorithm which compromises training, calculation of error, and modifying weights and then testing the given image.and make decisions based on this nearness. This proposed work has employed the MLP technique to identify the symbols, excellent results were obtained for a number of widely used Unicode Tamil font types.

  18. A Stable Control of a Cstr with Input Multiplicities Using Artificial Neural Network Based Narma-l2

    Directory of Open Access Journals (Sweden)

    Prabhaker Reddy Ginuga

    2014-01-01

    Full Text Available In this paper, the Neural network based NARMA-L2 controller is analyzed to an isothermal continuous stirred tank reactor (CSTR which exhibits input multiplicities in space velocity on product of B. i.e., two values of space velocity will give the same value of product B. The Performance of Neural network based NARMA-L2 controller and conventional PI controller have been evaluated through simulation studies. As the NARMA-L2 controller provides always the two values of space velocity for control action and by selecting the value nearer to the operating point, it is found to give stable and better responses than conventional PI controller. The PI controller results in unstable condition or switch over from initial lower input space velocity to higher input space velocity vice versa. Thus, NARMA-L2 controller is found to overcome the control problems of PI controller due to the input multiplicities.

  19. Neural Network based Consumption Forecasting

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    2016-01-01

    This paper describe a Neural Network based method for consumption forecasting. This work has been financed by the The ENCOURAGE project. The aims of The ENCOURAGE project is to develop embedded intelligence and integration technologies that will directly optimize energy use in buildings and enable...

  20. Efficacy of an artificial neural network-based approach to endoscopic ultrasound elastography in diagnosis of focal pancreatic masses

    DEFF Research Database (Denmark)

    Săftoiu, Adrian; Vilmann, Peter; Gorunescu, Florin;

    2012-01-01

    By using strain assessment, real-time endoscopic ultrasound (EUS) elastography provides additional information about a lesion's characteristics in the pancreas. We assessed the accuracy of real-time EUS elastography in focal pancreatic lesions using computer-aided diagnosis by artificial neural...... network analysis....

  1. Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

    Science.gov (United States)

    Chattopadhyay, Surajit; Chattopadhyay, Goutami

    2012-10-01

    In the work discussed in this paper we considered total ozone time series over Kolkata (22°34'10.92″N, 88°22'10.92″E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.

  2. Mixed integer non-linear programming and Artificial Neural Network based approach to ancillary services dispatch in competitive electricity markets

    International Nuclear Information System (INIS)

    Highlights: • Ancillary services market management. • Ancillary services requirements forecast based on Artificial Neural Network. • Ancillary services clearing mechanisms without complex bids and with complex bids. - Abstract: Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids

  3. Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters.

    Science.gov (United States)

    Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei

    2015-01-01

    Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915 measured samples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rate and heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08.

  4. Tracking the events in the coverage of wireless sensor networks based on artificial neural-networks algorithms

    Institute of Scientific and Technical Information of China (English)

    BAI Rong-gang; QU Yu-gui; LIN Zhi-ting; WANG Qing-hua; ZHAO Bao-hua

    2006-01-01

    Sensor deployment is an important problem in mobile wireless sensor networks.This paper presents a distributed self-spreading deployment algorithm (SOMDA) for mobile sensors based on artificial neural-networks selforganizing maps algorithm.During the deployment,the nodes compete to track the event and cooperate to form an ordered topology.After going through the algorithm,the statistical distribution of the nodes approaches that of the events in the interest area.The performance of the algorithm is evaluated by the covered percentage of region/events,the detecting ability and the energy equalization of the networks.The simulation results indicate that SOMDA outperforms uniform and random deployment with lossless coverage,enhancive detecting ability and significant energy equalization.

  5. Fast Prediction of HCCI and PCCI Combustion with an Artificial Neural Network-Based Chemical Kinetic Model

    Energy Technology Data Exchange (ETDEWEB)

    Piggott, W T; Aceves, S M; Flowers, D L; Chen, J Y

    2007-09-26

    We have added the capability to look at in-cylinder fuel distributions using a previously developed ignition model within a fluid mechanics code (KIVA3V) that uses an artificial neural network (ANN) to predict ignition (The combined code: KIVA3V-ANN). KIVA3V-ANN was originally developed and validated for analysis of Homogeneous Charge Compression Ignition (HCCI) combustion, but it is also applicable to the more difficult problem of Premixed Charge Compression Ignition (PCCI) combustion. PCCI combustion refers to cases where combustion occurs as a nonmixing controlled, chemical kinetics dominated, autoignition process, where the fuel, air, and residual gas mixtures are not necessarily as homogeneous as in HCCI combustion. This paper analyzes the effects of introducing charge non-uniformity into a KIVA3V-ANN simulation. The results are compared to experimental results, as well as simulation results using a more physically representative and computationally intensive code (KIVA3V-MPI-MZ), which links a fluid mechanics code to a multi-zone detailed chemical kinetics solver. The results indicate that KIVA3V-ANN produces reasonable approximations to the more accurate KIVA3V-MPI-MZ at a much reduced computational cost.

  6. Artificial neural network based calibrations for the prediction of galactic [NII] $\\lambda$ 6584 and H$\\alpha$ line luminosities

    CERN Document Server

    Teimoorinia, Hossein

    2014-01-01

    The artificial neural network (ANN) is a well-established mathematical technique for data prediction, based on the identification of correlations and pattern recognition in input training sets. We present the application of ANNs to predict the emission line luminosities of H$\\alpha$ and [NII] $\\lambda$6584 in galaxies. These important spectral diagnostics are used for metallicities, active galactic nuclei (AGN) classification and star formation rates, yet are shifted into the infra-red for galaxies above $z \\sim 0.5$, or may not be covered in spectra with limited wavelength coverage. The ANN is trained with a large sample of emission line galaxies selected from the Sloan Digital Sky Survey using various combinations of emission lines and stellar mass. The ANN is tested for galaxies dominated by both star formation and AGN; in both cases the H$\\alpha$ and [NII] $\\lambda$6584 line luminosities can be predicted with a scatter $\\sigma <$ 0.1 dex. We also show that the performance of the ANN does not depend sig...

  7. INDIA’S ELECTRICITY DEMAND FORECAST USING REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORKS BASED ON PRINCIPAL COMPONENTS

    Directory of Open Access Journals (Sweden)

    S. Saravanan

    2012-07-01

    Full Text Available Power System planning starts with Electric load (demand forecasting. Accurate electricity load forecasting is one of the most important challenges in managing supply and demand of the electricity, since the electricity demand is volatile in nature; it cannot be stored and has to be consumed instantly. The aim of this study deals with electricity consumption in India, to forecast future projection of demand for a period of 19 years from 2012 to 2030. The eleven input variables used are Amount of CO2 emission, Population, Per capita GDP, Per capita gross national income, Gross Domestic savings, Industry, Consumer price index, Wholesale price index, Imports, Exports and Per capita power consumption. A new methodology based on Artificial Neural Networks (ANNs using principal components is also used. Data of 29 years used for training and data of 10 years used for testing the ANNs. Comparison made with multiple linear regression (based on original data and the principal components and ANNs with original data as input variables. The results show that the use of ANNs with principal components (PC is more effective.

  8. Artificial Neural Network based control for PV/T panel to track optimum thermal and electrical power

    International Nuclear Information System (INIS)

    Highlights: ► We establish a state model of PV/T panel. ► We study the effect of mass flow rate on PV/T efficiency. ► A real time PV/T control algorithm is proposed. ► A model based optimal thermal and electrical power operation point is tracked. - Abstract: As solar energy is intermittent, many algorithms and electronics have been developed to track the maximum power generation from photovoltaic and thermal panels. Following technological advances, these panels are gathered into one unit: PV/T system. PV/T delivers simultaneously two kinds of power: electrical power and thermal power. Nevertheless, no control systems have been developed in order to track maximum power generation from PV/T system. This paper suggests a PV/T control algorithm based on Artificial Neural Network (ANN) to detect the optimal power operating point (OPOP) by considering PV/T model behavior. The OPOP computes the optimum mass flow rate of PV/T for a considered irradiation and ambient temperature. Simulation results demonstrate great concordance between OPOP model based calculation and ANN outputs.

  9. An Artificial Neural Network-Based Algorithm for Evaluation of Fatigue Crack Propagation Considering Nonlinear Damage Accumulation

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2016-06-01

    Full Text Available In the aerospace and aviation sectors, the damage tolerance concept has been applied widely so that the modeling analysis of fatigue crack growth has become more and more significant. Since the process of crack propagation is highly nonlinear and determined by many factors, such as applied stress, plastic zone in the crack tip, length of the crack, etc., it is difficult to build up a general and flexible explicit function to accurately quantify this complicated relationship. Fortunately, the artificial neural network (ANN is considered a powerful tool for establishing the nonlinear multivariate projection which shows potential in handling the fatigue crack problem. In this paper, a novel fatigue crack calculation algorithm based on a radial basis function (RBF-ANN is proposed to study this relationship from the experimental data. In addition, a parameter called the equivalent stress intensity factor is also employed as training data to account for loading interaction effects. The testing data is then placed under constant amplitude loading with different stress ratios or overloads used for model validation. Moreover, the Forman and Wheeler equations are also adopted to compare with our proposed algorithm. The current investigation shows that the ANN-based approach can deliver a better agreement with the experimental data than the other two models, which supports that the RBF-ANN has nontrivial advantages in handling the fatigue crack growth problem. Furthermore, it implies that the proposed algorithm is possibly a sophisticated and promising method to compute fatigue crack growth in terms of loading interaction effects.

  10. A Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid

    Directory of Open Access Journals (Sweden)

    Ashfaq Ahmad

    2015-12-01

    Full Text Available In the operation of a smart grid (SG, day-ahead load forecasting (DLF is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off between execution time and forecast accuracy. The newly-proposed DLF model will be able to accurately predict the load of the next day with a fair enough execution time. Our proposed model consists of three modules; the data preparation module, feature selection and the forecast module. The first module makes the historical load curve compatible with the feature selection module. The second module removes redundant and irrelevant features from the input data. The third module, which consists of an artificial neural network (ANN, predicts future load on the basis of selected features. Moreover, the forecast module uses a sigmoid function for activation and a multi-variate auto-regressive model for weight updating during the training process. Simulations are conducted in MATLAB to validate the performance of our newly-proposed DLF model in terms of accuracy and execution time. Results show that our proposed modified feature selection and modified ANN (m(FS + ANN-based model for SGs is able to capture the non-linearity(ies in the history load curve with 97 . 11 % accuracy. Moreover, this accuracy is achieved at the cost of a fair enough execution time, i.e., we have decreased the average execution time of the existing FS + ANN-based model by 38 . 50 % .

  11. BP Neural Network Based on Artificial Bee Colony Algorithm%基于人工蜂群的BP神经网络算法

    Institute of Scientific and Technical Information of China (English)

    李卫华; 徐涛; 李小梨

    2012-01-01

    The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Artificial Bee Colony Algorithm, which based on foraging behavior of honeybee swarms, is a new heuristic bionic algorithm and a typical kind of swarm intelligence algorithm. It is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony Algorithm was proposed to optimize the weight and threshold value of BP neural network. The result shows that the new algorithm improves the precision and expedites the convergence rate.%传统BP神经网络存在容易陷入局部极小点、收敛速度慢等缺点.人工蜂群算法是基于蜜蜂群体的觅食行为而提出的一种新的启发式仿生算法,属于典型的群体智能算法.它为全局优化算法,该算法简单、实现方便、鲁棒性强.针对BP神经网络算法的不足,提出利用人工蜂群算法交叉优化BP网络参数的权值和阈值,实验证明该优化算法确实提高了解的精度,加快了网络收敛速度.

  12. Estimation of global solar radiation using an artificial neural network based on an interpolation technique in southeast China

    Science.gov (United States)

    Zou, Ling; Wang, Lunche; Lin, Aiwen; Zhu, Hongji; Peng, Yuling; Zhao, Zhenzhen

    2016-08-01

    Solar radiation plays important roles in energy application, vegetation growth and climate change. Empirical relations and machine-learning methods have been widely used to estimate global solar radiation (GSR) in recent years. An artificial neural network (ANN) based on spatial interpolation is developed to estimate GSR in southeast China. The improved Bristow-Campbell (IBC) model and the improved Ångström-Prescott (IA-P) model are compared with the ANN model to explore the best model in solar radiation modeling. Daily meteorological parameters, such as sunshine duration hours, mean temperature, maximum temperature, minimum temperature, relative humidity, precipitation, air pressure, water vapor pressure, and wind speed, along with station-measured GSR and a daily surface GSR dataset over China obtained from the Data Assimilation and Modeling Center for Tibetan Multi-spheres (DAM), are used to predict GSR and to validate the models in this work. The ANN model with the network of 9-17-1 provides better accuracy than the two improved empirical models in GSR estimation. The root-mean-square error (RMSE), mean bias error (MBE), and determination coefficient (R2) are 2.65 MJ m-2, -0.94 MJ m-2, and 0.68 in the IA-P model; 2.19 MJ m-2, 1.11 MJ m-2, and 0.83 in the IBC model; 1.34 MJ m-2, -0.11 MJ m-2, and 0.91 in the ANN model, respectively. The regional monthly mean GSR in the measured dataset, DAM dataset, and ANN model is analyzed. The RMSE (RMSE %) is 1.07 MJ m-2 (8.91%) and the MBE (MBE %) is -0.62 MJ m-2 (-5.21%) between the measured and ANN-estimated GSR. The statistical errors of RMSE (RMSE %) are 0.91 MJ m-2 (7.28%) and those of MBE (MBE %) are -0.15 MJ m-2 (-1.20%) between DAM and ANN-modeled GSR. The correlation coefficients and R2 are larger than 0.95. The regional mean GSR is 12.58 MJ m-2. The lowest GSR is observed in the northwest area, and it increases from northwest to southeast. The annual mean GSR decreases by 0.02 MJ m-2 decade-1 over the entire

  13. Design and Implementation of Novel Artificial Neural Network Based Stock Market Forecasting System on Field-Programmable Gate Arrays

    Directory of Open Access Journals (Sweden)

    Md. S. Amin

    2011-01-01

    Full Text Available Problem statement: Multiagent system is very proficient and has rules well-suited for financial forecast with its neural network. In financial forecasting, the approach for rules extractions is less pertinent and involves algorithms which are complex. The unsupervised network method lacks in comprehensibility and leads to ambiguity. Approach: The application of neural network technology to real-time processing of financial market analysis demands the development of a new processing structure which allows efficient hardware realization of the neural network mechanism. This study describes the realization of neural network on FPGA device for stock market forecasting system. The stock market forecasting neural network architecture consists of three layers. These are input layer with three neurons, hidden layer with two neurons and output layer with one neuron. For both output layer and hidden layer neurons, Sigmoid transfer function is used. Neuron of each layer is modelled individually using behavioural VHDL. The layers are then connected using structural VHDL. This is followed by timing analysis and circuit synthesis for the validation, functionality and performance of the designated circuit. The designated portfolio is then programmed through download cable into the FPGA chip. Results: Kuala Lumpur Stock Exchange (KLSE index has been utilized for validating the usefulness of the completed prototype. Test on the sample of 100 data demonstrated an accuracy of 99.16% in predicting closing price of the KLSE index 10 days in advance. Conclusion: The test results are anticipated to be a higher rate of prediction for stock market analysis, thereby maintaining the high quality of supplying information in stock market business.

  14. Evolving Chart Pattern Sensitive Neural Network Based Forex Trading Agents

    CERN Document Server

    Sher, Gene I

    2011-01-01

    Though machine learning has been applied to the foreign exchange market for quiet some time now, and neural networks have been shown to yield good results, in modern approaches neural network systems are optimized through the traditional methods, and their input signals are vectors containing prices and other indicator elements. The aim of this paper is twofold, the presentation and testing of the application of topology and weight evolving artificial neural network (TWEANN) systems to automated currency trading, and the use of chart images as input to a geometrical regularity aware indirectly encoded neural network systems. This paper presents the benchmark results of neural network based automated currency trading systems evolved using TWEANNs, and compares the generalization capabilities of these direct encoded neural networks which use the standard price vector inputs, and the indirect (substrate) encoded neural networks which use chart images as input. The TWEANN algorithm used to evolve these currency t...

  15. Neural network based temporal video segmentation.

    Science.gov (United States)

    Cao, X; Suganthan, P N

    2002-01-01

    The organization of video information in video databases requires automatic temporal segmentation with minimal user interaction. As neural networks are capable of learning the characteristics of various video segments and clustering them accordingly, in this paper, a neural network based technique is developed to segment the video sequence into shots automatically and with a minimum number of user-defined parameters. We propose to employ growing neural gas (GNG) networks and integrate multiple frame difference features to efficiently detect shot boundaries in the video. Experimental results are presented to illustrate the good performance of the proposed scheme on real video sequences. PMID:12370954

  16. Neural Network Based 3D Surface Reconstruction

    Directory of Open Access Journals (Sweden)

    Vincy Joseph

    2009-11-01

    Full Text Available This paper proposes a novel neural-network-based adaptive hybrid-reflectance three-dimensional (3-D surface reconstruction model. The neural network combines the diffuse and specular components into a hybrid model. The proposed model considers the characteristics of each point and the variant albedo to prevent the reconstructed surface from being distorted. The neural network inputs are the pixel values of the two-dimensional images to be reconstructed. The normal vectors of the surface can then be obtained from the output of the neural network after supervised learning, where the illuminant direction does not have to be known in advance. Finally, the obtained normal vectors can be applied to integration method when reconstructing 3-D objects. Facial images were used for training in the proposed approach

  17. Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters

    OpenAIRE

    Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei

    2015-01-01

    Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was devel...

  18. Convolutional Neural Network Based dem Super Resolution

    Science.gov (United States)

    Chen, Zixuan; Wang, Xuewen; Xu, Zekai; Hou, Wenguang

    2016-06-01

    DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.

  19. Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.

    Science.gov (United States)

    Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

    2016-01-01

    Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO₂, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO₂ and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO₂; smoke and temperature; smoke, CO₂ and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%-92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition. PMID:27527175

  20. An Artificial Neural Network Based Analysis of Factors Controlling Particle Size in a Virgin Coconut Oil-Based Nanoemulsion System Containing Copper Peptide

    Science.gov (United States)

    Samson, Shazwani; Basri, Mahiran; Fard Masoumi, Hamid Reza; Abdul Malek, Emilia; Abedi Karjiban, Roghayeh

    2016-01-01

    A predictive model of a virgin coconut oil (VCO) nanoemulsion system for the topical delivery of copper peptide (an anti-aging compound) was developed using an artificial neural network (ANN) to investigate the factors that influence particle size. Four independent variables including the amount of VCO, Tween 80: Pluronic F68 (T80:PF68), xanthan gum and water were the inputs whereas particle size was taken as the response for the trained network. Genetic algorithms (GA) were used to model the data which were divided into training sets, testing sets and validation sets. The model obtained indicated the high quality performance of the neural network and its capability to identify the critical composition factors for the VCO nanoemulsion. The main factor controlling the particle size was found out to be xanthan gum (28.56%) followed by T80:PF68 (26.9%), VCO (22.8%) and water (21.74%). The formulation containing copper peptide was then successfully prepared using optimum conditions and particle sizes of 120.7 nm were obtained. The final formulation exhibited a zeta potential lower than -25 mV and showed good physical stability towards centrifugation test, freeze-thaw cycle test and storage at temperature 25°C and 45°C. PMID:27383135

  1. An Artificial Neural Network Based Analysis of Factors Controlling Particle Size in a Virgin Coconut Oil-Based Nanoemulsion System Containing Copper Peptide.

    Directory of Open Access Journals (Sweden)

    Shazwani Samson

    Full Text Available A predictive model of a virgin coconut oil (VCO nanoemulsion system for the topical delivery of copper peptide (an anti-aging compound was developed using an artificial neural network (ANN to investigate the factors that influence particle size. Four independent variables including the amount of VCO, Tween 80: Pluronic F68 (T80:PF68, xanthan gum and water were the inputs whereas particle size was taken as the response for the trained network. Genetic algorithms (GA were used to model the data which were divided into training sets, testing sets and validation sets. The model obtained indicated the high quality performance of the neural network and its capability to identify the critical composition factors for the VCO nanoemulsion. The main factor controlling the particle size was found out to be xanthan gum (28.56% followed by T80:PF68 (26.9%, VCO (22.8% and water (21.74%. The formulation containing copper peptide was then successfully prepared using optimum conditions and particle sizes of 120.7 nm were obtained. The final formulation exhibited a zeta potential lower than -25 mV and showed good physical stability towards centrifugation test, freeze-thaw cycle test and storage at temperature 25°C and 45°C.

  2. An Artificial Neural Network Based Analysis of Factors Controlling Particle Size in a Virgin Coconut Oil-Based Nanoemulsion System Containing Copper Peptide.

    Science.gov (United States)

    Samson, Shazwani; Basri, Mahiran; Fard Masoumi, Hamid Reza; Abdul Malek, Emilia; Abedi Karjiban, Roghayeh

    2016-01-01

    A predictive model of a virgin coconut oil (VCO) nanoemulsion system for the topical delivery of copper peptide (an anti-aging compound) was developed using an artificial neural network (ANN) to investigate the factors that influence particle size. Four independent variables including the amount of VCO, Tween 80: Pluronic F68 (T80:PF68), xanthan gum and water were the inputs whereas particle size was taken as the response for the trained network. Genetic algorithms (GA) were used to model the data which were divided into training sets, testing sets and validation sets. The model obtained indicated the high quality performance of the neural network and its capability to identify the critical composition factors for the VCO nanoemulsion. The main factor controlling the particle size was found out to be xanthan gum (28.56%) followed by T80:PF68 (26.9%), VCO (22.8%) and water (21.74%). The formulation containing copper peptide was then successfully prepared using optimum conditions and particle sizes of 120.7 nm were obtained. The final formulation exhibited a zeta potential lower than -25 mV and showed good physical stability towards centrifugation test, freeze-thaw cycle test and storage at temperature 25°C and 45°C.

  3. An Artificial Neural Network Based Analysis of Factors Controlling Particle Size in a Virgin Coconut Oil-Based Nanoemulsion System Containing Copper Peptide.

    Science.gov (United States)

    Samson, Shazwani; Basri, Mahiran; Fard Masoumi, Hamid Reza; Abdul Malek, Emilia; Abedi Karjiban, Roghayeh

    2016-01-01

    A predictive model of a virgin coconut oil (VCO) nanoemulsion system for the topical delivery of copper peptide (an anti-aging compound) was developed using an artificial neural network (ANN) to investigate the factors that influence particle size. Four independent variables including the amount of VCO, Tween 80: Pluronic F68 (T80:PF68), xanthan gum and water were the inputs whereas particle size was taken as the response for the trained network. Genetic algorithms (GA) were used to model the data which were divided into training sets, testing sets and validation sets. The model obtained indicated the high quality performance of the neural network and its capability to identify the critical composition factors for the VCO nanoemulsion. The main factor controlling the particle size was found out to be xanthan gum (28.56%) followed by T80:PF68 (26.9%), VCO (22.8%) and water (21.74%). The formulation containing copper peptide was then successfully prepared using optimum conditions and particle sizes of 120.7 nm were obtained. The final formulation exhibited a zeta potential lower than -25 mV and showed good physical stability towards centrifugation test, freeze-thaw cycle test and storage at temperature 25°C and 45°C. PMID:27383135

  4. Analysis of pull-in instability of geometrically nonlinear microbeam using radial basis artificial neural network based on couple stress theory.

    Science.gov (United States)

    Heidari, Mohammad; Heidari, Ali; Homaei, Hadi

    2014-01-01

    The static pull-in instability of beam-type microelectromechanical systems (MEMS) is theoretically investigated. Two engineering cases including cantilever and double cantilever microbeam are considered. Considering the midplane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. By selecting a range of geometric parameters such as beam lengths, width, thickness, gaps, and size effect, we identify the static pull-in instability voltage. A MAPLE package is employed to solve the nonlinear differential governing equations to obtain the static pull-in instability voltage of microbeams. Radial basis function artificial neural network with two functions has been used for modeling the static pull-in instability of microcantilever beam. The network has four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data, employed for training the network, and capabilities of the model have been verified in predicting the pull-in instability behavior. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach. The results reveal significant influences of size effect and geometric parameters on the static pull-in instability voltage of MEMS.

  5. Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study

    Directory of Open Access Journals (Sweden)

    Sun Gao

    2009-09-01

    Full Text Available Abstract Background Coal workers' pneumoconiosis (CWP is a preventable, but not fully curable occupational lung disease. More and more coal miners are likely to be at risk of developing CWP owing to an increase in coal production and utilization, especially in developing countries. Coal miners with different occupational categories and durations of dust exposure may be at different levels of risk for CWP. It is necessary to identify and classify different levels of risk for CWP in coal miners with different work histories. In this way, we can recommend different intervals for medical examinations according to different levels of risk for CWP. Our findings may provide a basis for further emending the measures of CWP prevention and control. Methods The study was performed using longitudinal retrospective data in the Tiefa Colliery in China. A three-layer artificial neural network with 6 input variables, 15 neurons in the hidden layer, and 1 output neuron was developed in conjunction with coal miners' occupational exposure data. Sensitivity and ROC analyses were adapted to explain the importance of input variables and the performance of the neural network. The occupational characteristics and the probability values predicted were used to categorize coal miners for their levels of risk for CWP. Results The sensitivity analysis showed that influence of the duration of dust exposure and occupational category on CWP was 65% and 67%, respectively. The area under the ROC in 3 sets was 0.981, 0.969, and 0.992. There were 7959 coal miners with a probability value Conclusion The duration of dust exposure and occupational category were the two most important factors for CWP. Coal miners at different levels of risk for CWP could be classified by the three-layer neural network analysis based on occupational history.

  6. Role of neutral wind in the performance of artificial neural-network based TEC models at diverse longitudes in the low latitudes

    Science.gov (United States)

    Sur, D.; Ray, S.; Paul, A.

    2015-03-01

    The equatorial ionosphere is characterized by (i) large values of total electron content (TEC) and sharp latitudinal gradients of TEC, (ii) steep temporal variation of TEC, (iii) large diurnal variation of TEC, and (iv) postsunset secondary enhancement of TEC. These features cause major limitations in the accuracy of standard ionospheric TEC models in this region. Three artificial neural-network (ANN) based models have been developed based on real-time low-latitude TEC data along 77°E, 88°E, and 121°E longitudes in the region between the magnetic equator and locations beyond the northern crest of equatorial ionization anomaly to predict the vertical TEC values. ANN models have shown more accurate predictions than other standard ionospheric TEC models like International Reference Ionosphere, Parameterized Ionospheric Model, and NeQuick. The effects of the neutral wind in the variation of TEC are significant and have been incorporated as inputs to these ANN models. The outputs with neutral wind incorporated shows better correspondence with measured TEC than the models without neutral wind inputs. The longitudinally separated models have been used to find any longitudinal differences in TEC along equatorial regions. The causes behind the longitudinal differences in TEC and its diurnal variations in these regions have been explained in terms of the geomagnetic declination and inclination angles along with the role of zonal wind.

  7. Artificial neural network-based exploration of gene-nutrient interactions in folate and xenobiotic metabolic pathways that modulate susceptibility to breast cancer.

    Science.gov (United States)

    Naushad, Shaik Mohammad; Ramaiah, M Janaki; Pavithrakumari, Manickam; Jayapriya, Jaganathan; Hussain, Tajamul; Alrokayan, Salman A; Gottumukkala, Suryanarayana Raju; Digumarti, Raghunadharao; Kutala, Vijay Kumar

    2016-04-15

    In the current study, an artificial neural network (ANN)-based breast cancer prediction model was developed from the data of folate and xenobiotic pathway genetic polymorphisms along with the nutritional and demographic variables to investigate how micronutrients modulate susceptibility to breast cancer. The developed ANN model explained 94.2% variability in breast cancer prediction. Fixed effect models of folate (400 μg/day) and B12 (6 μg/day) showed 33.3% and 11.3% risk reduction, respectively. Multifactor dimensionality reduction analysis showed the following interactions in responders to folate: RFC1 G80A × MTHFR C677T (primary), COMT H108L × CYP1A1 m2 (secondary), MTR A2756G (tertiary). The interactions among responders to B12 were RFC1G80A × cSHMT C1420T and CYP1A1 m2 × CYP1A1 m4. ANN simulations revealed that increased folate might restore ER and PR expression and reduce the promoter CpG island methylation of extra cellular superoxide dismutase and BRCA1. Dietary intake of folate appears to confer protection against breast cancer through its modulating effects on ER and PR expression and methylation of EC-SOD and BRCA1. PMID:26784656

  8. Neural Network Based Model for Predicting Housing Market Performance

    Institute of Scientific and Technical Information of China (English)

    Ahmed Khalafallah

    2008-01-01

    The United States real estate market is currently facing its worst hit in two decades due to the slowdown of housing sales. The most affected by this decline are real estate investors and home develop-ers who are currently struggling to break-even financially on their investments. For these investors, it is of utmost importance to evaluate the current status of the market and predict its performance over the short-term in order to make appropriate financial decisions. This paper presents the development of artificial neu-ral network based models to support real estate investors and home developers in this critical task. The pa-per describes the decision variables, design methodology, and the implementation of these models. The models utilize historical market performance data sets to train the artificial neural networks in order to pre-dict unforeseen future performances. An application example is analyzed to demonstrate the model capabili-ties in analyzing and predicting the market performance. The model testing and validation showed that the error in prediction is in the range between -2% and +2%.

  9. Radiolarian artificial neural network based paleo sea surface water temperature and salinity changes during the last glacial cycle in the Timor Sea, Indian Ocean

    Science.gov (United States)

    Gupta, S. M.; Malmgren, B. A.

    2015-12-01

    The western Pacific water enters into the Timor Sea (tropical Indian Ocean) by the thermohaline conveyor belt, and this region is under the influence of the SW monsoon. The higher precipitation during the monsoon rains lower the surface salinity in the north-eastern Indian Ocean towards the Bay of Bengal; whereas, the Arabian Sea remains highly saline due to higher evaporation in the region surrounding Arabian deserts. The salinity contrast in the northern Indian Ocean is very unique, and the radiolarian micro-zooplanktons living in the surface water serve a very good proxy for the monsoonal changes in the surface sea-water temperature (SST) and salinity in the geological past. We studied radiolarian faunal variation in the core MD01-2378, located at ~13oS and ~121oE (1783 m water depth), at the inlet of the thermohaline circulation into the Timor Sea. We applied the modern radiolarian based artificial neural networks (ANNs) (Gupta and Malmgren, 2009) to derive the SST and salinity during August-October for the last 140 ka (the full last glacial cycle). Based on the mean estimates of the 10 ANNs, the root mean square error in prediction (RMSEP) for SST is ~1.4oC with correlation between observed and estimated values r=0.98 (Gupta and Malmgren, 2009). Similarly, the RMSEP is 0.3 psu (r=0.94) for the salinity estimates. We derived paleo-SSTs and salinity values using modern radiolarian ANNs and the fossil radiolarian data generated from the core for the last 140-ka (Fig.1). The age model of the core is based on δ18O benthic oxygen isotope stratigraphy and 21 AMS 14C ages up to ~30-ka (Holbourn et al., 2005). Paleo SST-summer varied between 22-28.5oC, and it is in very good agreement with the δ18O benthic record of Holbourn et al. (2005) defining the Last Glacial Maximum (~24 ka) and the Eemian (~125 ka) stages. The salinity fluctuated between 34-35 psu, and compared well with oxygen isotope record representing the LGM and Eemian periods. We gratefully acknowledge

  10. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  11. 基于人工神经网络的土工合成材料加筋挡墙临界高度预测模型%Artificial neural networks-based model for forecasting critical height of GRW

    Institute of Scientific and Technical Information of China (English)

    周建萍; 闫澍旺

    2002-01-01

    提出了一种基于人工神经网络(ANN)技术的加筋挡墙设计高度预测方法.通过分析挡墙失效的原因,确定了7个主要因素作为网络的输入神经元.收集23组挡墙离心模型试验数据,2组足尺试验数据,1组实际工程的破坏数据,共26组样本作为训练及检验样本,建立了可用于加筋挡墙设计高度预测的径向基函数网络(RBFN)及误差反传网络(BPN)模型.结果表明径向基函数网络在学习速度,预测准确性及网络推广能力方面均优于BP网络,本文方法可用于加筋支挡结构的设计参考.%This paper presents an artificial neural networks-based approach for predicting the critical height of GRW. Seven major affecting factors have been used for analyzing the general failure cause. A radial basis function neural network (RBFN), as well as a back propagation neural network (BPN) for comparison, is trained and tested using 23 series of centrifuge model test data, 2 full-scale test data, and prototype date of a practical project. The modeling results indicated that the RBFN is much better than the BPN on learning speed, prediction accuracy and generalization ability. The paper provides a reference for GRW design.

  12. Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Kapil Nahar

    2012-12-01

    Full Text Available An artificial neural network is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons working in unison to solve specific problems. Ann’s, like people, learn by example.

  13. Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Kapil Nahar

    2012-12-01

    Full Text Available An artificial neural network is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons working in unison to solve specific problems.Ann’s, like people, learn by example.

  14. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  15. A Neural Network-Based Interval Pattern Matcher

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2015-07-01

    Full Text Available One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising.

  16. Turing Computation with Recurrent Artificial Neural Networks

    OpenAIRE

    Carmantini, Giovanni S; Graben, Peter beim; Desroches, Mathieu; Rodrigues, Serafim

    2015-01-01

    We improve the results by Siegelmann & Sontag (1995) by providing a novel and parsimonious constructive mapping between Turing Machines and Recurrent Artificial Neural Networks, based on recent developments of Nonlinear Dynamical Automata. The architecture of the resulting R-ANNs is simple and elegant, stemming from its transparent relation with the underlying NDAs. These characteristics yield promise for developments in machine learning methods and symbolic computation with continuous time d...

  17. Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine

    Science.gov (United States)

    Acharya, Nachiketa; Shrivastava, Nitin Anand; Panigrahi, B. K.; Mohanty, U. C.

    2014-09-01

    The south peninsular part of India gets maximum amount of rainfall during the northeast monsoon (NEM) season [October to November (OND)] which is the primary source of water for the agricultural activities in this region. A nonlinear method viz., Extreme learning machine (ELM) has been employed on general circulation model (GCM) products to make the multi-model ensemble (MME) based estimation of NEM rainfall (NEMR). The ELM is basically is an improved learning algorithm for the single feed-forward neural network (SLFN) architecture. The 27 year (1982-2008) lead-1 (using initial conditions of September for forecasting the mean rainfall of OND) hindcast runs (1982-2008) from seven GCM has been used to make MME. The improvement of the proposed method with respect to other regular MME (simple arithmetic mean of GCMs (EM) and singular value decomposition based multiple linear regressions based MME) has been assessed through several skill metrics like Spread distribution, multiplicative bias, prediction errors, the yield of prediction, Pearson's and Kendal's correlation coefficient and Wilmort's index of agreement. The efficiency of ELM estimated rainfall is established by all the stated skill scores. The performance of ELM in extreme NEMR years, out of which 4 years are characterized by deficit rainfall and 5 years are identified as excess, is also examined. It is found that the ELM could expeditiously capture these extremes reasonably well as compared to the other MME approaches.

  18. Advanced neural network-based computational schemes for robust fault diagnosis

    CERN Document Server

    Mrugalski, Marcin

    2014-01-01

    The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

  19. Hopfield neural network based on ant system

    Institute of Scientific and Technical Information of China (English)

    洪炳镕; 金飞虎; 郭琦

    2004-01-01

    Hopfield neural network is a single layer feedforward neural network. Hopfield network requires some control parameters to be carefully selected, else the network is apt to converge to local minimum. An ant system is a nature inspired meta heuristic algorithm. It has been applied to several combinatorial optimization problems such as Traveling Salesman Problem, Scheduling Problems, etc. This paper will show an ant system may be used in tuning the network control parameters by a group of cooperated ants. The major advantage of this network is to adjust the network parameters automatically, avoiding a blind search for the set of control parameters.This network was tested on two TSP problems, 5 cities and 10 cities. The results have shown an obvious improvement.

  20. A Neural Network-based ARX Model of Virgo Noise

    OpenAIRE

    Barone, F.; Rosa, R; Eleuteri, A.; Garufi, F.; Milano, L; Tagliaferri, R.

    1999-01-01

    In this paper a Neural Network based approach is presented to identify the noise in the VIRGO context. VIRGO is an experiment to detect Gravitational Waves by means of a Laser Interferometer. Preliminary results appear to be very promising for data analysis of realistic Interferometer outputs.

  1. Neural Network Based Intelligent Sootblowing System

    Energy Technology Data Exchange (ETDEWEB)

    Mark Rhode

    2005-04-01

    . Due to the composition of coal, particulate matter is also a by-product of coal combustion. Modern day utility boilers are usually fitted with electrostatic precipitators to aid in the collection of particulate matter. Although extremely efficient, these devices are sensitive to rapid changes in inlet mass concentration as well as total mass loading. Traditionally, utility boilers are equipped with devices known as sootblowers, which use, steam, water or air to dislodge and clean the surfaces within the boiler and are operated based upon established rule or operator's judgment. Poor sootblowing regimes can influence particulate mass loading to the electrostatic precipitators. The project applied a neural network intelligent sootblowing system in conjunction with state-of-the-art controls and instruments to optimize the operation of a utility boiler and systematically control boiler slagging/fouling. This optimization process targeted reduction of NOx of 30%, improved efficiency of 2% and a reduction in opacity of 5%. The neural network system proved to be a non-invasive system which can readily be adapted to virtually any utility boiler. Specific conclusions from this neural network application are listed below. These conclusions should be used in conjunction with the specific details provided in the technical discussions of this report to develop a thorough understanding of the process.

  2. Neural-network-based speed controller for induction motors using inverse dynamics model

    Science.gov (United States)

    Ahmed, Hassanein S.; Mohamed, Kamel

    2016-08-01

    Artificial Neural Networks (ANNs) are excellent tools for controller design. ANNs have many advantages compared to traditional control methods. These advantages include simple architecture, training and generalization and distortion insensitivity to nonlinear approximations and nonexact input data. Induction motors have many excellent features, such as simple and rugged construction, high reliability, high robustness, low cost, minimum maintenance, high efficiency, and good self-starting capabilities. In this paper, we propose a neural-network-based inverse model for speed controllers for induction motors. Simulation results show that the ANNs have a high tracing capability.

  3. What are artificial neural networks?

    DEFF Research Database (Denmark)

    Krogh, Anders

    2008-01-01

    Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb......Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb...

  4. Comment on 'Artificial neural network based modeling of heated catalytic converter performance' by M. Ali Akcayol and Can Cinar [Applied Thermal Engineering 25 (2005) 2341

    Energy Technology Data Exchange (ETDEWEB)

    Sha, W. [Metals Research Group, School of Planning, Architecture and Civil Engineering, The Queen' s University of Belfast, Belfast BT7 1NN (United Kingdom)

    2007-02-15

    A paper has been published in Applied Thermal Engineering, using feed-forward artificial neural network (ANN) in the modeling of heated catalytic converter performance. The present paper attempts to discuss and comment on the paper. The amount of data used in the paper are not enough to determine the number of fitting parameters in the network. Therefore, the model is not mathematically sound or justified. The conclusion is that ANN modeling should be used with care and enough data. (author)

  5. A neural network based seafloor classification using acoustic backscatter

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.

    This paper presents a study results of the Artificial Neural Network (ANN) architectures [Self-Organizing Map (SOM) and Multi-Layer Perceptron (MLP)] using single beam echosounding data. The single beam echosounder, operable at 12 kHz, has been used...

  6. Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

    Science.gov (United States)

    Zhang, Yanjun; Tao, Gang; Chen, Mou

    2016-09-01

    This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.

  7. Feature Selection for Neural Network Based Stock Prediction

    Science.gov (United States)

    Sugunnasil, Prompong; Somhom, Samerkae

    We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search through the generated set with hill climbing approach. The self-organizing map based stock prediction model is utilized as the prediction method. We conduct the experiment on data sets of the Microsoft Corporation, General Electric Co. and Ford Motor Co. The results show that our feature selection method can improve the efficiency of the neural network based stock prediction.

  8. Modeling of Relative Humidity Using Artificial Neural Network

    OpenAIRE

    Samer AlSadi; Tamer Khatib

    2012-01-01

    This paper presents a relative humidity predictions using feedforward artificial neural network (FFNN). Relative humidity values obtained from weather records for Malaysia are used in training the FFNNs. The prediction of the relative humidity is in terms of Sun shine ration and cloud cover. However, three statistical parameters, namely, mean absolute percentage error, MAPE, mean bias error, MBE, and root mean square error, RMSE are used to evaluate the neural networks. Based on results, the ...

  9. A fuzzy logic and neural network-based intelligent mine winder-motion control system. Part 1

    Energy Technology Data Exchange (ETDEWEB)

    Szklarski, L.; Fijalkowski, B. [Akademia Gorniczo-Hutnicza, Krakow (Poland)

    1994-09-01

    A fuzzy logic and neural network-based intelligent mine winder-motion control system would appear to be a useful contribution to mining automation where efforts are continually being made to reduce costs. This paper briefly describes macroelectronic and microelectronic mine winder propulsion and dispulsion spheres discussing their advantages and disadvantages. A fuzzy logic and neural network-based intelligent mine winder-motion control used in modern real-time expert hypersphere techniques is described. Amongst examples presented is an electromechanically-driven and fluidomechanically-braked intelligent mine winder. This incorporates a four-groove rope motorised pulley-fitted with an AC-AC, AC-DC-AC or DC-AC macrocommutator pulley-hub motor, and an artificial intelligence application-specific integrated circuit, which is fuzzy logic and neural network-based programmable, together with a learning PID microcontroller. 13 refs., 2 figs.

  10. Image Compression of Neural Network Based on Corner Block

    Directory of Open Access Journals (Sweden)

    Wenjing Zhang

    2014-01-01

    Full Text Available Most information received by the human is acquired through vision. However, image has the largest data amount in three information forms. If the image is not compressed, high transmission rate for digital image transmission and tremendous capacity for digital image storage can hinder the development of digital image. For example, for a color image whose resolution rate is 1280×1024, each pixel needs 24B for storage, and the total data amount is about 3.75MB. If the earth satellite transmits the acquired image to the earth at 30 frames per second, the transmitting data size in 1 second is about 112.5MB. Under the condition of the existing communication capacity, if the image is not compressed, the real-time transmission of most multimedia information can’t be completed. High-speed transmission and storage of digital image has become the biggest obstacle of promoting digital image communication. So it is necessary to compress image. Data compression not only can rapidly transmit various information sources, improve the utilization rage of information channel and reduce transmitted power, but also can save energy and reduce storage capacity. More and more attentions of people have been paid to the application of artificial neural network to image compression, the reason for which is that artificial neural network has good fault tolerance, self-organization and adaptivity compared with traditional compression methods. So the predetermined data coding algorithm is not needed in the process of image compression. Neural network can independently complete the image coding and compression according to the characteristics of image. The paper combines corner detection technology with artificial neural network image compression, and designs a new neural network image compression encoding based on corner block with reasonable structure, high compression rate and rapid convergence rate

  11. Artificial neural networks in NDT

    International Nuclear Information System (INIS)

    Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)

  12. 基于改进布谷鸟搜索的人工神经网络及其性能仿真%Artificial Neural Network Based on Improved Cuckoo Search and Its Performance Simulation

    Institute of Scientific and Technical Information of China (English)

    倪百秀; 张翠翠; 周本达

    2015-01-01

    Cuckoo search is a novel meta-heuristic algorithm based on bionics. It has good ability to search for global optimum ,but suffers from slow searching speed in the last iterations and poor accuracy. The cross-entropy method is embedded into cuckoo search algorithm and an improved cuckoo search algorithm is introduced. The testing results of benchmark functions show the improved algorithm obtains good performance of convergence speed and high accuracy. The proposed algorithm is employed as a new training method for artificial neural network. , and the experimental results show that the proposed algorithm outperforms for training neural networks in terms of converging speed and avoiding local minima. Finally ,the artificial neural network with learning algorithm based on the improved algorithm is employed to forecast total population of China.%布谷鸟搜索(CS)算法是一种新型的基于仿生学原理的元启发式算法,具有很好的全局优化能力,但其存在后期收敛速度慢、计算精度不高等不足。通过将交叉熵(CE)方法嵌入到CS中构建一种改进的CS算法,基准测试函数集的测试结果表明改进算法收敛速度和计算精度都有了明显提高。用改进的算法实现对人工神经网络的训练,实验结果显示新算法训练的神经网络收敛速度更快,能有效避开局部极小。最后用所建立的人工神经网络对中国人口总量进行了预测。

  13. Artificial neural networks in medicine

    Energy Technology Data Exchange (ETDEWEB)

    Keller, P.E.

    1994-07-01

    This Technology Brief provides an overview of artificial neural networks (ANN). A definition and explanation of an ANN is given and situations in which an ANN is used are described. ANN applications to medicine specifically are then explored and the areas in which it is currently being used are discussed. Included are medical diagnostic aides, biochemical analysis, medical image analysis and drug development.

  14. Neural Network-Based Active Control for Offshore Platforms

    Institute of Scientific and Technical Information of China (English)

    周亚军; 赵德有

    2003-01-01

    A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i.e. the inherent robustness, fault tolerance, and generalized capability of its parallel massive interconnection structure, the active structural control of offshore platforms under random waves is accomplished by use of the BP neural network model. The neural network is trained offline with the data generated from numerical analysis, and it simulates the process of Classical Linear Quadratic Regular Control for the platform under random waves. After the learning phase, the trained network has learned about the nonlinear dynamic behavior of the active control system, and is capable of predicting the active control forces of the next time steps. The results obtained show that the active control is feasible and effective, and it finally overcomes time delay owing to the robustness, fault tolerance, and generalized capability of artificial neural network.

  15. Neural Network Based Parking via Google Map Guidance

    Directory of Open Access Journals (Sweden)

    A.Saranya

    2015-02-01

    Full Text Available Intelligent transportation systems (ITS focus to generate and spread creative services related to different transport modes for traffic management and hence enables the passenger informed about the traffic and to use the transport networks in a better way. Intelligent Trip Modeling System (ITMS uses machine learning to forecast the traveling speed profile for a selected route based on the traffic information available at the trip starting time. The intelligent Parking Information Guidance System provides an eminent Neural Network based intelligence system which provides automatic allocate ion of parking's through the Global Information system across the path of the users travel. In this project using efficient lookup table searches and a Lagrange-multiplier bisection search, Computational Optimized Allocation Algorithm converges faster to the optimal solution than existing techniques. The purpose of this project is to simulate and implement a real parking environment that allocates vacant parking slots using Allocation algorithm.

  16. Neural network based feed-forward high density associative memory

    Science.gov (United States)

    Daud, T.; Moopenn, A.; Lamb, J. L.; Ramesham, R.; Thakoor, A. P.

    1987-01-01

    A novel thin film approach to neural-network-based high-density associative memory is described. The information is stored locally in a memory matrix of passive, nonvolatile, binary connection elements with a potential to achieve a storage density of 10 to the 9th bits/sq cm. Microswitches based on memory switching in thin film hydrogenated amorphous silicon, and alternatively in manganese oxide, have been used as programmable read-only memory elements. Low-energy switching has been ascertained in both these materials. Fabrication and testing of memory matrix is described. High-speed associative recall approaching 10 to the 7th bits/sec and high storage capacity in such a connection matrix memory system is also described.

  17. Neural network based cluster creation in the ATLAS Pixel Detector

    CERN Document Server

    Andreazza, A; The ATLAS collaboration

    2012-01-01

    The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to reconstruct the location where a charged particle passed through the sensor. The resolution given by individual pixel sizes is significantly improved by using the information from the charge sharing be- tween pixels. Such analog cluster creation techniques have been used by the ATLAS experiment for many years to obtain an excellent performance. How- ever, in dense environments, such as those inside high-energy jets, clusters have an increased probability of merging the charge deposited by multiple particles. Recently, a neural network based algorithm which estimates both the cluster position and whether a cluster should be split has been developed for the ATLAS Pixel Detector. The algorithm significantly reduces ambigui- ties in the assignment of pixel detector measurement to tracks and improves the position accuracy with respect to standard techniques by taking into account the 2-dimensional charge distribution.

  18. Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

    Directory of Open Access Journals (Sweden)

    Manjunath Patel Gowdru Chandrashekarappa

    2014-01-01

    Full Text Available The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN and genetic algorithm neural network (GA-NN. The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs.

  19. Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study - article no. 366

    Energy Technology Data Exchange (ETDEWEB)

    Liu, H.B.; Tang, Z.F.; Yang, Y.L.; Weng, D.; Sun, G.; Duan, Z.W.; Chen, J. [China Medical University, Shenyang (China). School of Public Health

    2009-09-15

    Coal workers' pneumoconiosis (CWP) is a preventable, but not fully curable occupational lung disease. More and more coal miners are likely to be at risk of developing CWP owing to an increase in coal production and utilization, especially in developing countries. Coal miners with different occupational categories and durations of dust exposure may be at different levels of risk for CWP. It is necessary to identify and classify different levels of risk for CWP in coal miners with different work histories. In this way, we can recommend different intervals for medical examinations according to different levels of risk for CWP. Our findings may provide a basis for further emending the measures of CWP prevention and control. The study was performed using longitudinal retrospective data in the Tiefa Colliery in China. A three-layer artificial neural network with 6 input variables, 15 neurons in the hidden layer, and 1 output neuron was developed in conjunction with coal miners' occupational exposure data. The duration of dust exposure and occupational category were the two most important factors for CWP. Coal miners at different levels of risk for CWP could be classified by the three-layer neural network analysis based on occupational history.

  20. Fuzzy neural network based on a Sigmoid chaotic neuron

    Institute of Scientific and Technical Information of China (English)

    Zhang Yi; Wang Xing-Yuan

    2012-01-01

    The theories of intelligent information processing are urgently needed for the rapid development of modem science.In this paper,a novel fuzzy chaotic neural network,which is the combination of fuzzy logic system,artificial neuralnetwork system,and chaotic system,is proposed.We design its model structure which is based on the Sigmoid map,derive its mathematical model,and analyse its chaotic characteristics.Finally the relationship between the accuracy of map and the membership function is illustrated by simulation.

  1. Hardware Prototyping of Neural Network based Fetal Electrocardiogram Extraction

    Science.gov (United States)

    Hasan, M. A.; Reaz, M. B. I.

    2012-01-01

    The aim of this paper is to model the algorithm for Fetal ECG (FECG) extraction from composite abdominal ECG (AECG) using VHDL (Very High Speed Integrated Circuit Hardware Description Language) for FPGA (Field Programmable Gate Array) implementation. Artificial Neural Network that provides efficient and effective ways of separating FECG signal from composite AECG signal has been designed. The proposed method gives an accuracy of 93.7% for R-peak detection in FHR monitoring. The designed VHDL model is synthesized and fitted into Altera's Stratix II EP2S15F484C3 using the Quartus II version 8.0 Web Edition for FPGA implementation.

  2. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  3. Introduction to Concepts in Artificial Neural Networks

    Science.gov (United States)

    Niebur, Dagmar

    1995-01-01

    This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

  4. Application of Artificial Neural Networks for Predicting Generated Wind Power

    OpenAIRE

    Vijendra Singh

    2016-01-01

    This paper addresses design and development of an artificial neural network based system for prediction of wind energy produced by wind turbines. Now in the last decade, renewable energy emerged as an additional alternative source for electrical power generation. We need to assess wind power generation capacity by wind turbines because of its non-exhaustible nature. The power generation by electric wind turbines depends on the speed of wind, flow direction, fluctuations, density of air, gener...

  5. Automated neural network-based instrument validation system

    Science.gov (United States)

    Xu, Xiao

    2000-10-01

    In a complex control process, instrument calibration is periodically performed to maintain the instruments within the calibration range, which assures proper control and minimizes down time. Instruments are usually calibrated under out-of-service conditions using manual calibration methods, which may cause incorrect calibration or equipment damage. Continuous in-service calibration monitoring of sensors and instruments will reduce unnecessary instrument calibrations, give operators more confidence in instrument measurements, increase plant efficiency or product quality, and minimize the possibility of equipment damage during unnecessary manual calibrations. In this dissertation, an artificial neural network (ANN)-based instrument calibration verification system is designed to achieve the on-line monitoring and verification goal for scheduling maintenance. Since an ANN is a data-driven model, it can learn the relationships among signals without prior knowledge of the physical model or process, which is usually difficult to establish for the complex non-linear systems. Furthermore, the ANNs provide a noise-reduced estimate of the signal measurement. More importantly, since a neural network learns the relationships among signals, it can give an unfaulted estimate of a faulty signal based on information provided by other unfaulted signals; that is, provide a correct estimate of a faulty signal. This ANN-based instrument verification system is capable of detecting small degradations or drifts occurring in instrumentation, and preclude false control actions or system damage caused by instrument degradation. In this dissertation, an automated scheme of neural network construction is developed. Previously, the neural network structure design required extensive knowledge of neural networks. An automated design methodology was developed so that a network structure can be created without expert interaction. This validation system was designed to monitor process sensors plant

  6. Neural Network Based Color Recognition for Bobbin Sorting Machine

    Directory of Open Access Journals (Sweden)

    Mu Zhang

    2013-07-01

    Full Text Available Winding is a key process in the manufacturing process of textile industry. The normal and effective operation of winding process plays a very important role on the textiles’ quality and economic effects. At present, a large proportion of bobbins which collected from winder still have yarn left over. The bobbin recycling is severely limited and quick running of winder is seriously restricted, the invention of the the automatic bobbin sorting machine has solved this problem. The ability to distinguish bobbin which has yarn left over from the rest and the classification accuracy of color are the two important performance indicators for bobbin sorting machine. According to the development and application of the color recognition technology and the artificial intelligence method, this study proposes a novel color recognition method that based on BP neural networks. The result shows that the accuracy of color recognition reaches 98%.  

  7. Neural Network based Modeling and Simulation of Transformer Inrush Current

    Directory of Open Access Journals (Sweden)

    Puneet Kumar Singh

    2012-05-01

    Full Text Available Inrush current is a very important phenomenon which occurs during energization of transformer at no load due to temporary over fluxing. It depends on several factors like magnetization curve, resistant and inductance of primary winding, supply frequency, switching angle of circuit breaker etc. Magnetizing characteristics of core represents nonlinearity which requires improved nonlinearity solving technique to know the practical behavior of inrush current. Since several techniques still working on modeling of transformer inrush current but neural network ensures exact modeling with experimental data. Therefore, the objective of this study was to develop an Artificial Neural Network (ANN model based on data of switching angle and remanent flux for predicting peak of inrush current. Back Propagation with Levenberg-Marquardt (LM algorithm was used to train the ANN architecture and same was tested for the various data sets. This research work demonstrates that the developed ANN model exhibits good performance in prediction of inrush current’s peak with an average of percentage error of -0.00168 and for modeling of inrush current with an average of percentage error of -0.52913.

  8. Artificial neural networks in neurosurgery.

    Science.gov (United States)

    Azimi, Parisa; Mohammadi, Hasan Reza; Benzel, Edward C; Shahzadi, Sohrab; Azhari, Shirzad; Montazeri, Ali

    2015-03-01

    Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on the application of ANNs as a tool for assisting clinical decision-making in neurosurgery. A literature review of all full publications in English biomedical journals (1993-2013) was undertaken. The strategy included a combination of key words 'artificial neural networks', 'prognostic', 'brain', 'tumor tracking', 'head', 'tumor', 'spine', 'classification' and 'back pain' in the title and abstract of the manuscripts using the PubMed search engine. The major findings are summarized, with a focus on the application of ANNs for diagnostic and prognostic purposes. Finally, the future of ANNs in neurosurgery is explored. A total of 1093 citations were identified and screened. In all, 57 citations were found to be relevant. Of these, 50 articles were eligible for inclusion in this review. The synthesis of the data showed several applications of ANN in neurosurgery, including: (1) diagnosis and assessment of disease progression in low back pain, brain tumours and primary epilepsy; (2) enhancing clinically relevant information extraction from radiographic images, intracranial pressure processing, low back pain and real-time tumour tracking; (3) outcome prediction in epilepsy, brain metastases, lumbar spinal stenosis, lumbar disc herniation, childhood hydrocephalus, trauma mortality, and the occurrence of symptomatic cerebral vasospasm in patients with aneurysmal subarachnoid haemorrhage; (4) the use in the biomechanical assessments of spinal disease. ANNs can be effectively employed for diagnosis, prognosis and outcome prediction in neurosurgery.

  9. Neural Network Based Augmented Reality for Detection of Brain Tumor

    Directory of Open Access Journals (Sweden)

    P.Mithun

    2013-04-01

    Full Text Available The development in technology opened the door of fiction and reached reality. Major medical applications deals on robot-assisted surgery and image guided surgery. Because of this, substantial research is going on to implement Augmented Reality (AR in instruments which incorporate the surgeon’s intuitive capabilities. Augmented reality is the grouping of virtual entity or 3D stuffs which are overlapped on live camera feed information. The decisive aim of augmented reality is to enhancing the virtual video and a 3D object onto a real world on which it will raise the person’s conceptual understanding of the subject. In this paper we described a solution for initial prediction of tumour cells in MRI images of human brain using image processing technique the output of which will be the 3D slicedimage of the human brain. The sliced image is then virtually embedded on the top of human head during the time of surgery so that the surgeon can exactly locate the spot to be operated. Before augmenting the 3D sliced image Artificial neural network is used to select the appropriate image that contains tumor automatically in order to make the system more efficient.

  10. A neural network based reputation bootstrapping approach for service selection

    Science.gov (United States)

    Wu, Quanwang; Zhu, Qingsheng; Li, Peng

    2015-10-01

    With the concept of service-oriented computing becoming widely accepted in enterprise application integration, more and more computing resources are encapsulated as services and published online. Reputation mechanism has been studied to establish trust on prior unknown services. One of the limitations of current reputation mechanisms is that they cannot assess the reputation of newly deployed services as no record of their previous behaviours exists. Most of the current bootstrapping approaches merely assign default reputation values to newcomers. However, by this kind of methods, either newcomers or existing services will be favoured. In this paper, we present a novel reputation bootstrapping approach, where correlations between features and performance of existing services are learned through an artificial neural network (ANN) and they are then generalised to establish a tentative reputation when evaluating new and unknown services. Reputations of services published previously by the same provider are also incorporated for reputation bootstrapping if available. The proposed reputation bootstrapping approach is seamlessly embedded into an existing reputation model and implemented in the extended service-oriented architecture. Empirical studies of the proposed approach are shown at last.

  11. Neural network-based detection of pulmonary nodules on chest radiography; Identificazione mediante reti neurali dei noduli polmonari nel radiogramma del torace

    Energy Technology Data Exchange (ETDEWEB)

    Coppini, G. [Consiglio Nazionale delle Ricerche, Pisa (Italy). Ist. di Fisiologia Patologica; Valli, G. [Florence Univ., Florence (Italy). Dipt. di Ingegneria Elettronica; Falchini, M.; Stecco, A.; Bindi, A.; Carmignani, L. [Florence Univ., Florence (Italy). Dipt. di Fisiopatologia Clinica, Sezione di Radiodiagnostica

    1999-10-01

    In this report are investigated the capabilities of an artificial neural network-based Computer-Aided Diagnosis (CAD) system in improving early detection of pulmonary nodules on chest radiographs. [Italian] Valutazione di un sistema di riconoscimento automatico basato sulla tecnologia delle reti neruali artificiali per migliorare le possibilita' di rivelazione precoce dei noduli polmonari sul radiogramma toracico.

  12. Artificial neural networks in nuclear medicine

    International Nuclear Information System (INIS)

    An analysis of the accessible literature on the diagnostic applicability of artificial neural networks in coronary artery disease and pulmonary embolism appears to be comparative to the diagnosis of experienced doctors dealing with nuclear medicine. Differences in the employed models of artificial neural networks indicate a constant search for the most optimal parameters, which could guarantee the ultimate accuracy in neural network activity. The diagnostic potential within systems containing artificial neural networks proves this calculation tool to be an independent or/and an additional device for supporting a doctor's diagnosis of artery disease and pulmonary embolism. (author)

  13. Fuzzy Neural Network Based Traffic Prediction and Congestion Control in High-Speed Networks

    Institute of Scientific and Technical Information of China (English)

    费翔; 何小燕; 罗军舟; 吴介一; 顾冠群

    2000-01-01

    Congestion control is one of the key problems in high-speed networks, such as ATM. In this paper, a kind of traffic prediction and preventive congestion control scheme is proposed using neural network approach. Traditional predictor using BP neural network has suffered from long convergence time and dissatisfying error. Fuzzy neural network developed in this paper can solve these problems satisfactorily. Simulations show the comparison among no-feedback control scheme,reactive control scheme and neural network based control scheme.

  14. Introduction to artificial neural networks.

    Science.gov (United States)

    Grossi, Enzo; Buscema, Massimo

    2007-12-01

    The coupling of computer science and theoretical bases such as nonlinear dynamics and chaos theory allows the creation of 'intelligent' agents, such as artificial neural networks (ANNs), able to adapt themselves dynamically to problems of high complexity. ANNs are able to reproduce the dynamic interaction of multiple factors simultaneously, allowing the study of complexity; they can also draw conclusions on individual basis and not as average trends. These tools can offer specific advantages with respect to classical statistical techniques. This article is designed to acquaint gastroenterologists with concepts and paradigms related to ANNs. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data and from complex, dynamic, and multidimensional phenomena, which are often poorly predictable in the traditional 'cause and effect' philosophy. PMID:17998827

  15. Simulation and Optimization for Thermally Coupled Distillation Using Artificial Neural Network and Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    王延敏; 姚平经

    2003-01-01

    In this paper, a new approach using artificial neural network and genetic algorithm for the optimization of the thermally coupled distillation is presented. Mathematical model can be constructed with artificial neural network based on the simulation results with ASPEN PLUS. Modified genetic algorithm was used to optimize the model. With the proposed model and optimization arithmetic, mathematical model can be calculated, decision variables and target value can be reached automatically and quickly. A practical example is used to demonstrate the algorithm.

  16. Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

    Science.gov (United States)

    Xia, Youshen; Wang, Jun

    2015-07-01

    This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. PMID:25913233

  17. Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

    Science.gov (United States)

    Xia, Youshen; Wang, Jun

    2015-07-01

    This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction.

  18. Learning in Artificial Neural Systems

    Science.gov (United States)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  19. Application of Global Dynamic Reconfiguration in Artificial Neural Network System based on Field Programmable Gate Array

    Institute of Scientific and Technical Information of China (English)

    LI Wei; WANG Wei; MA Yi-mei; WANG Jin-hai

    2008-01-01

    Presented is a global dynamic reconfiguration design of an artificial neural network based on field programmable gate array(FPGA). Discussed are the dynamic reconfiguration principles and methods. Proposed is a global dynamic reconfiguration scheme using Xilinx FPGA and platform flash. Using the revision capabilities of Xilinx XCF32P platform flash, an artificial neural network based on Xilinx XC2V30P Virtex-Ⅱ can be reconfigured dynamically from back propagation(BP) learning algorithms to BP network testing algorithms. The experimental results indicate that the scheme is feasible, and that, using dynamic reconfiguration technology, FPGA resource utilization can be reduced remarkably.

  20. Neural Networks-Based Forecasting Regarding the Convergence Process of CEE Countries to the Eurozone

    Directory of Open Access Journals (Sweden)

    Magdalena RĂDULESCU

    2014-06-01

    Full Text Available In the crisis frame, many forecasts failed to provide well determined ratios. What we tried to explain in this paper is how some selected Central and Eastern European countries will perform in the near future: Romania, Bulgaria, Hungary, Poland and Czech Republic, using neural networks- based forecasting model which we created for the nominal and real convergence ratios. As a methodology, we propose the forecasting based on artificial neural network (ANN, using the well-known software tool GMDH Shell. For each output variable, we obtain a forecast model, according to previous values and other input related variables, and we applied the model to all countries. Our forecasts are much closer to the partial results of 2013 in the analyzed countries than the European Commission’s or other international organizations’ forecasts. The results of the forecast are important both for governments to design their financial strategies and for the investors in these selected countries. According to our results, the Czech Republic seems to be closer to achieve its nominal convergence in the next two years, but it faces great difficulties in the real convergence area, because it did not overpass the recession.

  1. Neural network-based H∞ filtering for nonlinear systems with time-delays

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    A novel H∞ design methodology for a neural network-based nonlinear filtering scheme is addressed.Firstly,neural networks are employed to approximate the nonlinearities.Next,the nonlinear dynamic system is represented by the mode-dependent linear difference inclusion (LDI).Finally,based on the LDI model,a neural network-based nonlinear filter (NNBNF) is developed to minimize the upper bound of H∞ gain index of the estimation error under some linear matrix inequality (LMI) constraints.Compared with the existing nonlinear filters,NNBNF is time-invariant and numerically tractable.The validity and applicability of the proposed approach are successfully demonstrated in an illustrative example.

  2. Implementation of neural network based non-linear predictive control

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole;

    1999-01-01

    of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...

  3. International Conference on Artificial Neural Networks (ICANN)

    CERN Document Server

    Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

    2015-01-01

    The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

  4. BRAIN TUMOR CLASSIFICATION USING NEURAL NETWORK BASED METHODS

    OpenAIRE

    Kalyani A. Bhawar*, Prof. Nitin K. Bhil

    2016-01-01

    MRI (Magnetic resonance Imaging) brain neoplasm pictures Classification may be a troublesome tasks due to the variance and complexity of tumors. This paper presents two Neural Network techniques for the classification of the magnetic resonance human brain images. The proposed Neural Network technique consists of 3 stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the options connected with tomography pictures victimization d...

  5. Neural network based speech synthesizer: A preliminary report

    Science.gov (United States)

    Villarreal, James A.; Mcintire, Gary

    1987-01-01

    A neural net based speech synthesis project is discussed. The novelty is that the reproduced speech was extracted from actual voice recordings. In essence, the neural network learns the timing, pitch fluctuations, connectivity between individual sounds, and speaking habits unique to that individual person. The parallel distributed processing network used for this project is the generalized backward propagation network which has been modified to also learn sequences of actions or states given in a particular plan.

  6. Artificial astrocytes improve neural network performance.

    Directory of Open Access Journals (Sweden)

    Ana B Porto-Pazos

    Full Text Available Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN and artificial neuron-glia networks (NGN to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  7. Neural Network Based Forecasting of Foreign Currency Exchange Rates

    Directory of Open Access Journals (Sweden)

    S. Kumar Chandar

    2014-06-01

    Full Text Available The foreign currency exchange market is the highest and most liquid of the financial markets, with an estimated $1 trillion traded every day. Foreign exchange rates are the most important economic indices in the international financial markets. The prediction of them poses many theoretical and experimental challenges. This paper reports empirical proof that a neural network model is applicable to the prediction of foreign exchange rates. The exchange rates between Indian Rupee and four other major currencies, Pound Sterling, US Dollar, Euro and Japanese Yen are forecast by the trained neural networks. The neural network was trained by three different learning algorithms using historical data to find the suitable algorithm for prediction. The forecasting performance of the proposed system is evaluated using three statistical metrics and compared. The results presented here demonstrate that significantly close prediction can be made without extensive knowledge of market data.

  8. Implementation of neural network based non-linear predictive

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole;

    1998-01-01

    The paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems including open loop unstable and non-minimum phase systems, but has also been proposed extended for the control of non-linear...... systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi......-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system....

  9. Clustering in mobile ad hoc network based on neural network

    Institute of Scientific and Technical Information of China (English)

    CHEN Ai-bin; CAI Zi-xing; HU De-wen

    2006-01-01

    An on-demand distributed clustering algorithm based on neural network was proposed. The system parameters and the combined weight for each node were computed, and cluster-heads were chosen using the weighted clustering algorithm, then a training set was created and a neural network was trained. In this algorithm, several system parameters were taken into account, such as the ideal node-degree, the transmission power, the mobility and the battery power of the nodes. The algorithm can be used directly to test whether a node is a cluster-head or not. Moreover, the clusters recreation can be speeded up.

  10. Hand Gesture and Neural Network Based Human Computer Interface

    Directory of Open Access Journals (Sweden)

    Aekta Patel

    2014-06-01

    Full Text Available Computer is used by every people either at their work or at home. Our aim is to make computers that can understand human language and can develop a user friendly human computer interfaces (HCI. Human gestures are perceived by vision. The research is for determining human gestures to create an HCI. Coding of these gestures into machine language demands a complex programming algorithm. In this project, We have first detected, recognized and pre-processing the hand gestures by using General Method of recognition. Then We have found the recognized image’s properties and using this, mouse movement, click and VLC Media player controlling are done. After that we have done all these functions thing using neural network technique and compared with General recognition method. From this we can conclude that neural network technique is better than General Method of recognition. In this, I have shown the results based on neural network technique and comparison between neural network method & general method.

  11. Neural network based PWM AC chopper fed induction motor drive

    Directory of Open Access Journals (Sweden)

    Venkatesan Jamuna

    2009-01-01

    Full Text Available In this paper, a new Simulink model for a neural network controlled PWM AC chopper fed single phase induction motor is proposed. Closed loop speed control is achieved using a neural network controller. To maintain a constant fluid flow with a variation in pressure head, drives like fan and pump are operated with closed loop speed control. The need to improve the quality and reliability of the drive circuit has increased because of the growing demand for improving the performance of motor drives. With the increased availability of MOSFET's and IGBT's, PWM converters can be used efficiently in low and medium power applications. From the simulation studies, it is seen that the PWM AC chopper has a better harmonic spectrum and lesser copper loss than the Phase controlled AC chopper. It is observed that the drive system with the proposed model produces better dynamic performance, reduced overshoot and fast transient response. .

  12. Evolving Chart Pattern Sensitive Neural Network Based Forex Trading Agents

    OpenAIRE

    Sher, Gene I.

    2011-01-01

    Though machine learning has been applied to the foreign exchange market for algorithmic trading for quiet some time now, and neural networks(NN) have been shown to yield positive results, in most modern approaches the NN systems are optimized through traditional methods like the backpropagation algorithm for example, and their input signals are price lists, and lists composed of other technical indicator elements. The aim of this paper is twofold: the presentation and testing of the applicati...

  13. Quantum Neural Network Based Machine Translator for Hindi to English

    OpenAIRE

    Ravi Narayan; Singh, V.P.; Chakraverty, S.

    2014-01-01

    This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze t...

  14. Configuration space control of a parallel delta robot with a neural network based inverse kinematics

    OpenAIRE

    Uzunovic, Tarik; Golubovic, Edin; Baran, Eray Abdurrahman; Şabanoviç, Asif; SABANOVIC, Asif

    2013-01-01

    This paper describes configuration space control of a Delta robot with a neural network based kinematics. Mathematical model of the kinematics for parallel Delta robot used for manipulation purposes in microfactory was validated, and experiments showed that this model is not describing “real” kinematics properly. Therefore a new solution for kinematics mapping had to be investigated. Solution was found in neural network utilization, and it was used to model robot's inverse kinematics. It show...

  15. Wavelet neural network based fault diagnosis in nonlinear analog circuits

    Institute of Scientific and Technical Information of China (English)

    Yin Shirong; Chen Guangju; Xie Yongle

    2006-01-01

    The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studied. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation(BP) neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.

  16. Wavelet Neural Network Based Traffic Prediction for Next Generation Network

    Institute of Scientific and Technical Information of China (English)

    Zhao Qigang; Li Qunzhan; He Zhengyou

    2005-01-01

    By using netflow traffic collecting technology, some traffic data for analysis are collected from a next generation network (NGN) operator. To build a wavelet basis neural network (NN), the Sigmoid function is replaced with the wavelet in NN. Then the wavelet multiresolution analysis method is used to decompose the traffic signal, and the decomposed component sequences are employed to train the NN. By using the methods, an NGN traffic prediction model is built to predict one day's traffic. The experimental results show that the traffic prediction method of wavelet NN is more accurate than that without using wavelet in the NGN traffic forecasting.

  17. Application of artificial neural network in market segmentation: A review on recent trends

    OpenAIRE

    Chattopadhyay, Manojit; Dan, Pranab K; Majumdar, Sitanath; Chakraborty, Partha Sarathi

    2012-01-01

    Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and propose...

  18. Neural Network based Vehicle Classification for Intelligent Traffic Control

    Directory of Open Access Journals (Sweden)

    Saeid Fazli

    2012-06-01

    Full Text Available Nowadays, number of vehicles has been increased and traditional systems of traffic controlling couldn’t be able to meet the needs that cause to emergence of Intelligent Traffic Controlling Systems. They improve controlling and urban management and increase confidence index in roads and highways. The goal of thisarticle is vehicles classification base on neural networks. In this research, it has been used a immovable camera which is located in nearly close height of the road surface to detect and classify the vehicles. The algorithm that used is included two general phases; at first, we are obtaining mobile vehicles in the traffic situations by using some techniques included image processing and remove background of the images and performing edge detection and morphology operations. In the second phase, vehicles near the camera areselected and the specific features are processed and extracted. These features apply to the neural networks as a vector so the outputs determine type of vehicle. This presented model is able to classify the vehicles in three classes; heavy vehicles, light vehicles and motorcycles. Results demonstrate accuracy of the algorithm and its highly functional level.

  19. Neural network based daily precipitation generator (NNGEN-P)

    Energy Technology Data Exchange (ETDEWEB)

    Boulanger, Jean-Philippe [LODYC, UMR CNRS/IRD/UPMC, Paris (France); University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Martinez, Fernando; Segura, Enrique C. [University of Buenos Aires, Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Penalba, Olga [University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina)

    2007-02-15

    Daily weather generators are used in many applications and risk analyses. The present paper explores the potential of neural network architectures to design daily weather generator models. Focusing this first paper on precipitation, we design a collection of neural networks (multi-layer perceptrons in the present case), which are trained so as to approximate the empirical cumulative distribution (CDF) function for the occurrence of wet and dry spells and for the precipitation amounts. This approach contributes to correct some of the biases of the usual two-step weather generator models. As compared to a rainfall occurrence Markov model, NNGEN-P represents fairly well the mean and standard deviation of the number of wet days per month, and it significantly improves the simulation of the longest dry and wet periods. Then, we compared NNGEN-P to three parametric distribution functions usually applied to fit rainfall cumulative distribution functions (Gamma, Weibull and double-exponential). A data set of 19 Argentine stations was used. Also, data corresponding to stations in the United States, in Europe and in the Tropics were included to confirm the results. One of the advantages of NNGEN-P is that it is non-parametric. Unlike other parametric function, which adapt to certain types of climate regimes, NNGEN-P is fully adaptive to the observed cumulative distribution functions, which, on some occasions, may present complex shapes. On-going works will soon produce an extended version of NNGEN to temperature and radiation. (orig.)

  20. Convolutional Neural Network Based Fault Detection for Rotating Machinery

    Science.gov (United States)

    Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Van de Walle, Rik; Van Hoecke, Sofie

    2016-09-01

    Vibration analysis is a well-established technique for condition monitoring of rotating machines as the vibration patterns differ depending on the fault or machine condition. Currently, mainly manually-engineered features, such as the ball pass frequencies of the raceway, RMS, kurtosis an crest, are used for automatic fault detection. Unfortunately, engineering and interpreting such features requires a significant level of human expertise. To enable non-experts in vibration analysis to perform condition monitoring, the overhead of feature engineering for specific faults needs to be reduced as much as possible. Therefore, in this article we propose a feature learning model for condition monitoring based on convolutional neural networks. The goal of this approach is to autonomously learn useful features for bearing fault detection from the data itself. Several types of bearing faults such as outer-raceway faults and lubrication degradation are considered, but also healthy bearings and rotor imbalance are included. For each condition, several bearings are tested to ensure generalization of the fault-detection system. Furthermore, the feature-learning based approach is compared to a feature-engineering based approach using the same data to objectively quantify their performance. The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier. The former achieves an accuracy of 93.61 percent and the latter an accuracy of 87.25 percent.

  1. Neural-network-based satellite tracking for deep space applications

    Science.gov (United States)

    Amoozegar, Farid; Ruggier, Charles

    2003-09-01

    NASA has been considering the use of Ka-band for deep space missions primarily for downlink telemetry applications. At such high frequencies, although the link will be expected to improve by a factor of four, the current Deep Space Network (DSN) antennas and transmitters would become less efficient due to higher equipment noise figures and antenna surface errors. Furthermore, the weather effect at Ka-band frequencies will dominate the degradations in link performance and tracking accuracy. At the lower frequencies, such as X-band, conventional CONSCAN or Monopulse tracking techniques can be used without much complexity, however, when utilizing Ka-band frequencies, the tracking of a spacecraft in deep space presents additional challenges. The objective of this paper is to provide a survey of neural network trends as applied to the tracking of spacecrafts in deep space at Ka-band under various weather conditions, and examine the trade-off between tracking accuracy and communication link performance.

  2. Recurrent neural networks-based multivariable system PID predictive control

    Institute of Scientific and Technical Information of China (English)

    ZHANG Yan; WANG Fanzhen; SONG Ying; CHEN Zengqiang; YUAN Zhuzhi

    2007-01-01

    A nonlinear proportion integration differentiation (PID) controller is proposed on the basis of recurrent neural networks,due to the difficulty of tuning the parameters of conventional PID controller.In the control process of nonlinear multivariable system,a decoupling controller was constructed,which took advantage of multi-nonlinear PID controllers in parallel.With the idea of predictive control,two multivariable predictive control strategies were established.One strategy involved the use of the general minimum variance control function on the basis of recursive multi-step predictive method.The other involved the adoption of multistep predictive cost energy to train the weights of the decoupling controller.Simulation studies have shown the efficiency of these strategies.

  3. Quantum neural network based machine translator for Hindi to English.

    Science.gov (United States)

    Narayan, Ravi; Singh, V P; Chakraverty, S

    2014-01-01

    This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation. PMID:24977198

  4. Artificial Neural Network Based Approach for short load forecasting

    OpenAIRE

    Rajesh Deshmukh; Dr. Amita Mahor

    2011-01-01

    Accurate models for electric power load forecasting are essential to the operation and planning of a power utility company. Load forecasting helps electric utility to make important decisions on trading of power, load switching, and infrastructure development. Load forecasts are extremely important for power utilizes ISOs, financial institutions, and other stakeholder of power sector. Short term load forecasting is a essential part of electric power system planning and operation forecasting m...

  5. Rule Extraction using Artificial Neural Networks

    CERN Document Server

    Kamruzzaman, S M

    2010-01-01

    Artificial neural networks have been successfully applied to a variety of business application problems involving classification and regression. Although backpropagation neural networks generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions are not as interpretable as those of decision trees. In many applications, it is desirable to extract knowledge from trained neural networks so that the users can gain a better understanding of the solution. This paper presents an efficient algorithm to extract rules from artificial neural networks. We use two-phase training algorithm for backpropagation learning. In the first phase, the number of hidden nodes of the network is determined automatically in a constructive fashion by adding nodes one after another based on the performance of the network on training data. In the second phase, the number of relevant input units of the network is determined using pruning algorithm. The ...

  6. Employing Neocognitron Neural Network Base Ensemble Classifiers To Enhance Efficiency Of Classification In Handwritten Digit Datasets

    Directory of Open Access Journals (Sweden)

    Neera Saxena

    2011-07-01

    Full Text Available This paper presents an ensemble of neo-cognitron neural network base classifiers to enhance the accuracy of the system, along the experimental results. The method offers lesser computational preprocessing in comparison to other ensemble techniques as it ex-preempts feature extraction process before feeding the data into base classifiers. This is achieved by the basic nature of neo-cognitron, it is a multilayer feed-forward neural network. Ensemble of such base classifiers gives class labels for each pattern that in turn is combined to give the final class label for that pattern. The purpose of this paper is not only to exemplify learning behaviour of neo-cognitron as base classifiers, but also to purport better fashion to combine neural network based ensemble classifiers.

  7. Seabed Classification Using BP Neural Network Based on GA

    Institute of Scientific and Technical Information of China (English)

    Yang Fanlin; Liu Jingnan

    2003-01-01

    Side scan sonar imaging is one of the advanced methods for seabed study. In order to be utilized in other projects, such as ocean engineering, the image needs to be classified according to the distributions of different classes of seabed materials. In this paper, seabed image is classified according to BP neural network, and Genetic Algorithm is adopted in train network in this paper. The feature vectors are average intensity, six statistics of texture and two dimensions of fractal. It considers not only the spatial correlation between different pixels, but also the terrain coarseness. The texture is denoted by the statistics of the co-occurrence matrix. Double Blanket algorithm is used to calculate dimension. Because a uniform fractal may not be sufficient to describe a seafloor, two dimensions are calculated respectively by the upper blanket and the lower blanket. However, in sonar image, fractal has directivity, i. e.there are different dimensions in different direction. Dimensions are different in acrosstrack and alongtrack, so the average of four directions is used to solve this problem. Finally, the real data verify the algorithm. In this paper, one hidden layer including six nodes is adopted. The BP network is rapidly and accurately convergent through GA. Correct classification rate is 92.5% in the result.

  8. Comparative Study of Backpropagation Algorithms in Neural Network Based Identification of Power System

    Directory of Open Access Journals (Sweden)

    Sheela Tiwari

    2013-08-01

    Full Text Available This paperexplores theapplicationof artificial neural networksfor online identification of a multimachinepower system.Arecurrent neural networkhas been proposedas the identifier of the two area, four machinesystemwhich is a benchmark system for studying electromechanical oscillations in multimachine powersystems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of thepaper is on investigating the performance of the variants of the Backpropagation algorithm in training theneural identifier. The paper also compares the performances of the neural identifiers trained usingvariantsof the Backpropagation algorithmover a wide range of operating conditions.The simulation resultsestablish a satisfactory performance of the trained neural identifiers in identification of the test powersystem

  9. Comparing artificial and biological dynamical neural networks

    Science.gov (United States)

    McAulay, Alastair D.

    2006-05-01

    Modern computers can be made more friendly and otherwise improved by making them behave more like humans. Perhaps we can learn how to do this from biology in which human brains evolved over a long period of time. Therefore, we first explain a commonly used biological neural network (BNN) model, the Wilson-Cowan neural oscillator, that has cross-coupled excitatory (positive) and inhibitory (negative) neurons. The two types of neurons are used for frequency modulation communication between neurons which provides immunity to electromagnetic interference. We then evolve, for the first time, an artificial neural network (ANN) to perform the same task. Two dynamical feed-forward artificial neural networks use cross-coupling feedback (like that in a flip-flop) to form an ANN nonlinear dynamic neural oscillator with the same equations as the Wilson-Cowan neural oscillator. Finally we show, through simulation, that the equations perform the basic neural threshold function, switching between stable zero output and a stable oscillation, that is a stable limit cycle. Optical implementation with an injected laser diode and future research are discussed.

  10. Prediction of Electrochemical Machining Process Parameters using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Hoda Hosny Abuzied

    2012-01-01

    Full Text Available Electrochemical machining (ECM is a non-traditional machining process used mainly to cut hard or difficult to cut metals, where the application of a more traditional process is not convenient. It offers several special advantages including higher machining rate, better precision and control, and a wider range of materials that can be machined. A suitable selection of machining parameters for the ECM process relies heavily on the operator’s technologies and experience because of their numerous and diverse range. Machining parameters provided by the machine tool builder cannot meet the operator’s requirements. So, artificial neural networks were introduced as an efficient approach to predict the values of resulting surface roughness and material removal rate. Many researchers usedartificial neural networks (ANN in improvement of ECM process and also in other nontraditional machining processes as well be seen in later sections. The present study is, initiated to predict values of some of resulting process parameters such as metal removal rate(MRR, and surface roughness (Ra using artificial neural networks based on variation of certain predominant parameters of an electrochemical broaching process such as applied voltage, feed rate and electrolyte flow rate. ANN was found to be an efficient approach as it reduced time & effort required to predict material removal rate & surface roughness if they were found experimentally using trial & error method. To validate the proposed approach the predicted values of surface roughness and material removal rate were compared with a previously obtained ones from the experimental work.

  11. Artificial neural interfaces for bionic cardiovascular treatments.

    Science.gov (United States)

    Kawada, Toru; Sugimachi, Masaru

    2009-01-01

    An artificial nerve, in the broad sense, may be conceptualized as a physical and logical interface system that reestablishes the information traffic between the central nervous system and peripheral organs. Studies on artificial nerves targeting the autonomic nervous system are in progress to explore new treatment strategies for several cardiovascular diseases. In this article, we will review our research targeting the autonomic nervous system to treat cardiovascular diseases. First, we identified the rule for decoding native sympathetic nerve activity into a heart rate using transfer function analysis, and established a framework for a neurally regulated cardiac pacemaker. Second, we designed a bionic baroreflex system to restore the baroreflex buffering function using electrical stimulation of the celiac ganglion in a rat model of orthostatic hypotension. Third, based on the hypothesis that autonomic imbalance aggravates chronic heart failure, we implanted a neural interface into the right vagal nerve and demonstrated that intermittent vagal stimulation significantly improved the survival rate in rats with chronic heart failure following myocardial infarction. Although several practical problems need to be resolved, such as those relating to the development of electrodes feasible for long-term nerve activity recording, studies of artificial neural interfaces with the autonomic nervous system have great possibilities in the field of cardiovascular treatment. We expect further development of artificial neural interfaces as novel strategies to cope with cardiovascular diseases resistant to conventional therapeutics.

  12. Assessing Landslide Hazard Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, Farzad; Choobbasti, Asskar Janalizadeh; Barari, Amin;

    2011-01-01

    neural network has been developed for use in the stability evaluation of slopes under various geological conditions and engineering requirements. The Artificial neural network model of this research uses slope characteristics as input and leads to the output in form of the probability of failure...... and factor of safety. It can be stated that the trained neural networks are capable of predicting the stability of slopes and safety factor of landslide hazard in study area with an acceptable level of confidence. Landslide hazard analysis and mapping can provide useful information for catastrophic loss...... failure" which is main concentration of the current research and "liquefaction failure". Shear failures along shear planes occur when the shear stress along the sliding surfaces exceed the effective shear strength. These slides have been referred to as landslide. An expert system based on artificial...

  13. PID Neural Network Based Speed Control of Asynchronous Motor Using Programmable Logic Controller

    Directory of Open Access Journals (Sweden)

    MARABA, V. A.

    2011-11-01

    Full Text Available This paper deals with the structure and characteristics of PID Neural Network controller for single input and single output systems. PID Neural Network is a new kind of controller that includes the advantages of artificial neural networks and classic PID controller. Functioning of this controller is based on the update of controller parameters according to the value extracted from system output pursuant to the rules of back propagation algorithm used in artificial neural networks. Parameters obtained from the application of PID Neural Network training algorithm on the speed model of the asynchronous motor exhibiting second order linear behavior were used in the real time speed control of the motor. Programmable logic controller (PLC was used as real time controller. The real time control results show that reference speed successfully maintained under various load conditions.

  14. Neural Network based Control of SG based Standalone Generating System with Energy Storage for Power Quality Enhancement

    Science.gov (United States)

    Nayar, Priya; Singh, Bhim; Mishra, Sukumar

    2016-09-01

    An artificial intelligence based control algorithm is used in solving power quality problems of a diesel engine driven synchronous generator with automatic voltage regulator and governor based standalone system. A voltage source converter integrated with a battery energy storage system is employed to mitigate the power quality problems. An adaptive neural network based signed regressor control algorithm is used for the estimation of the fundamental component of load currents for control of a standalone system with load leveling as an integral feature. The developed model of the system performs accurately under varying load conditions and provides good dynamic response to the step changes in loads. The real time performance is achieved using MATLAB along with simulink/simpower system toolboxes and results adhere to an IEEE-519 standard for power quality enhancement.

  15. Development of a Neural Network-Based Renewable Energy Forecasting Framework for Process Industries

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Soobin; Ryu, Jun-Hyung; Hodge, Bri-Mathias; Lee, In-Beum

    2016-06-25

    This paper presents a neural network-based forecasting framework for photovoltaic power (PV) generation as a decision-supporting tool to employ renewable energies in the process industry. The applicability of the proposed framework is illustrated by comparing its performance against other methodologies such as linear and nonlinear time series modelling approaches. A case study of an actual PV power plant in South Korea is presented.

  16. Neural network-based software sensor: Data set design and application to a continuous pulp digester

    OpenAIRE

    Dufour, Pascal; Bhartiya, Sharad; Dhurjati, Prasad S.; Doyle Iii, Francis J.

    2005-01-01

    The authors acknowledge the Control Engineering Practice journal for his copyright policies & self-archiving. More open archives publications of Pascal Dufour are available on: http://hal.archives-ouvertes.fr/DUFOUR-PASCAL-C-3926-2008 International audience A neural network based strategy for detection of feedstock variations in a continuous pulp digester is presented. A feedforward two-layer perceptron network is trained to detect and isolate unmeasured variations in the feedstock. Tra...

  17. Neural Network Based Feedback Linearization Control of an Unmanned Aerial Vehicle

    Institute of Scientific and Technical Information of China (English)

    Dan Necsulescu; Yi-Wu Jiang; Bumsoo Kim

    2007-01-01

    This paper presents a flight control design for an unmanned aerial vehicle (UAV) using a nonlinear autoregressive moving average (NARMA-L2) neural network based feedback linearization and output redefinition technique. The UAV investigated is nonminimum phase. The output redefinition technique is used in such a way that the resulting system to be inverted is a minimum phase system. The NARMA-L2 neural network is trained off-line for forward dynamics of the UAV model with redefined output and is then inverted to force the real output to approximately track a command input. Simulation results show that the proposed approaches have good performance.

  18. A Predictive Neural Network-Based Cascade Control for pH Reactors

    Directory of Open Access Journals (Sweden)

    Mujahed AlDhaifallah

    2016-01-01

    Full Text Available This paper is concerned with the development of predictive neural network-based cascade control for pH reactors. The cascade structure consists of a master control loop (fuzzy proportional-integral and a slave one (predictive neural network. The master loop is chosen to be more accurate but slower than the slave one. The strong features found in cascade structure have been added to the inherent features in model predictive neural network. The neural network is used to alleviate modeling difficulties found with pH reactor and to predict its behavior. The parameters of predictive algorithm are determined using an optimization algorithm. The effectiveness and feasibility of the proposed design have been demonstrated using MatLab.

  19. Artificial neural network applications in ionospheric studies

    Directory of Open Access Journals (Sweden)

    L. R. Cander

    1998-06-01

    Full Text Available The ionosphere of Earth exhibits considerable spatial changes and has large temporal variability of various timescales related to the mechanisms of creation, decay and transport of space ionospheric plasma. Many techniques for modelling electron density profiles through entire ionosphere have been developed in order to solve the "age-old problem" of ionospheric physics which has not yet been fully solved. A new way to address this problem is by applying artificial intelligence methodologies to current large amounts of solar-terrestrial and ionospheric data. It is the aim of this paper to show by the most recent examples that modern development of numerical models for ionospheric monthly median long-term prediction and daily hourly short-term forecasting may proceed successfully applying the artificial neural networks. The performance of these techniques is illustrated with different artificial neural networks developed to model and predict the temporal and spatial variations of ionospheric critical frequency, f0F2 and Total Electron Content (TEC. Comparisons between results obtained by the proposed approaches and measured f0F2 and TEC data provide prospects for future applications of the artificial neural networks in ionospheric studies.

  20. Artificial neural networks in neutron dosimetry

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado, G.A.; Perales M, W.A.; Robles R, J.A. [Unidades Academicas de Estudios Nucleares, UAZ, A.P. 336, 98000 Zacatecas (Mexico); Gallego, E.; Lorente, A. [Depto. de Ingenieria Nuclear, Universidad Politecnica de Madrid, (Spain)

    2005-07-01

    An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the {chi}{sup 2}- test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  1. Livermore Big Artificial Neural Network Toolkit

    Energy Technology Data Exchange (ETDEWEB)

    2016-07-01

    LBANN is a toolkit that is designed to train artificial neural networks efficiently on high performance computing architectures. It is optimized to take advantages of key High Performance Computing features to accelerate neural network training. Specifically it is optimized for low-latency, high bandwidth interconnects, node-local NVRAM, node-local GPU accelerators, and high bandwidth parallel file systems. It is built on top of the open source Elemental distributed-memory dense and spars-direct linear algebra and optimization library that is released under the BSD license. The algorithms contained within LBANN are drawn from the academic literature and implemented to work within a distributed-memory framework.

  2. Application of Artificial Neural Networks for Predicting Generated Wind Power

    Directory of Open Access Journals (Sweden)

    Vijendra Singh

    2016-03-01

    Full Text Available This paper addresses design and development of an artificial neural network based system for prediction of wind energy produced by wind turbines. Now in the last decade, renewable energy emerged as an additional alternative source for electrical power generation. We need to assess wind power generation capacity by wind turbines because of its non-exhaustible nature. The power generation by electric wind turbines depends on the speed of wind, flow direction, fluctuations, density of air, generator hours, seasons of an area, and wind turbine position. During a particular season, wind power generation access can be increased. In such a case, wind energy generation prediction is crucial for transmission of generated wind energy to a power grid system. It is advisable for the wind power generation industry to predict wind power capacity to diagnose it. The present paper proposes an effort to apply artificial neural network technique for measurement of the wind energy generation capacity by wind farms in Harshnath, Sikar, Rajasthan, India.

  3. A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

    OpenAIRE

    Si-Yao Fu; Guo-Sheng Yang; Xin-Kai Kuai

    2012-01-01

    In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural net...

  4. Artificial Neural Network for Displacement Vectors Determination

    Directory of Open Access Journals (Sweden)

    P. Bohmann

    1997-09-01

    Full Text Available An artificial neural network (NN for displacement vectors (DV determination is presented in this paper. DV are computed in areas which are essential for image analysis and computer vision, in areas where are edges, lines, corners etc. These special features are found by edges operators with the following filtration. The filtration is performed by a threshold function. The next step is DV computation by 2D Hamming artificial neural network. A method of DV computation is based on the full search block matching algorithms. The pre-processing (edges finding is the reason why the correlation function is very simple, the process of DV determination needs less computation and the structure of the NN is simpler.

  5. Analysis of SSR Using Artificial Neural Networks

    OpenAIRE

    Nagabhushana, BS; Chandrasekharaiah, HS

    1996-01-01

    Artificial neural networks (ANNs) are being advantageously applied to power system analysis problems. They possess the ability to establish complicated input-output mappings through a learning process, without any explicit programming. In this paper, an ANN based method for subsynchronous resonance (SSR) analysis is presented. The designed ANN outputs a measure of the possibility of the occurrence of SSR and is fully trained to accommodate the variations of power system parameters over the en...

  6. Practical introduction to artificial neural networks

    OpenAIRE

    Bougrain, Laurent

    2004-01-01

    What are they ? What for are they ? How to use them ? This article wants to answer these three fundamental questions about artificial neural networks that every engineer interested by this machine learning technique asks to oneself. We present the most useful architectures. We explain how to train them using a supervised or an unsupervised learning depending on the task we want to do : regression, discrimination or clustering. What kind of data can one use and how to prepare them ? Finally, w...

  7. Web Page Categorization Using Artificial Neural Networks

    OpenAIRE

    S. M. Kamruzzaman

    2010-01-01

    Web page categorization is one of the challenging tasks in the world of ever increasing web technologies. There are many ways of categorization of web pages based on different approach and features. This paper proposes a new dimension in the way of categorization of web pages using artificial neural network (ANN) through extracting the features automatically. Here eight major categories of web pages have been selected for categorization; these are business & economy, education, government, en...

  8. Artificial Neural Networks in Stellar Astronomy

    Directory of Open Access Journals (Sweden)

    R. K. Gulati

    2001-01-01

    Full Text Available Next generation of optical spectroscopic surveys, such as the Sloan Digital Sky Survey and the 2 degree field survey, will provide large stellar databases. New tools will be required to extract useful information from these. We show the applications of artificial neural networks to stellar databases. In another application of this method, we predict spectral and luminosity classes from the catalog of spectral indices. We assess the importance of such methods for stellar populations studies.

  9. POWER SCALABLE IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS

    OpenAIRE

    Modi, Sankalp; Wilson, Peter; Brown, Andrew

    2005-01-01

    As the use of Artificial Neural Network(ANN) in mobile embedded devices gets more pervasive, power consumption of ANN hardware is becoming a major limiting factor. Although considerable research efforts are now directed towards low-power implementations of ANN, the issue of dynamic power scalability of the implemented design has been largely overlooked. In this paper, we discuss the motivation and basic principles for implementing power scaling in ANN Hardware. With the help of a simple examp...

  10. CONTROL OF NONLINEAR PROCESS USING NEURAL NETWORK BASED MODEL PREDICTIVE CONTROL

    Directory of Open Access Journals (Sweden)

    Dr.A.TRIVEDI

    2011-04-01

    Full Text Available This paper presents a Neural Network based Model Predictive Control (NNMPC strategy to control nonlinear process. Multilayer Perceptron Neural Network (MLP is chosen to represent a Nonlinear Auto Regressive with eXogenous signal (NARX model of a nonlinear system. NARX dynamic model is based on feed-forward architecture and offers good approximation capabilities along with robustness and accuracy. Based on the identified neural model, a generalized predictive control (GPC algorithm is implemented to control the composition in acontinuous stirred tank reactor (CSTR, whose parameters are optimally determined by solving quadratic performance index using well known Levenberg-Marquardt and Quasi-Newton algorithm. NNMPC is tuned by selecting few horizon parameters and weighting factor. The tracking performance of the NNMPC is tested using different amplitude function as a reference signal on CSTR application. Also the robustness and performance is tested in the presence of disturbance on random reference signal.

  11. Neural network based semi-active control strategy for structural vibration mitigation with magnetorheological damper

    DEFF Research Database (Denmark)

    Bhowmik, Subrata

    2011-01-01

    This paper presents a neural network based semi-active control method for a rotary type magnetorheological (MR) damper. The characteristics of the MR damper are described by the classic Bouc-Wen model, and the performance of the proposed control method is evaluated in terms of a base exited shear......-displacement trajectories. The proposed neural network controller is therefore trained based on data derived from these desired forcedisplacement curves, where the optimal relation between friction force level and response amplitude is determined explicitly by simply maximizing the damping ratio of the targeted vibration...... to determine the damper current based on the derived optimal damper force. For that reason an inverse MR damper model is also designed based on the neural network identification of the particular rotary MR damper. The performance of the proposed controller is compared to that of an optimal pure viscous damper...

  12. Neutron spectrometry using artificial neural networks

    International Nuclear Information System (INIS)

    An artificial neural network has been designed to obtain neutron spectra from Bonner spheres spectrometer count rates. The neural network was trained using 129 neutron spectra. These include spectra from isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra based on mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. The re-binned spectra and the UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and their respective spectra were used as output during the neural network training. After training, the network was tested with the Bonner spheres count rates produced by folding a set of neutron spectra with the response matrix. This set contains data used during network training as well as data not used. Training and testing was carried out using the Matlab(R) program. To verify the network unfolding performance, the original and unfolded spectra were compared using the root mean square error. The use of artificial neural networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated with this ill-conditioned problem

  13. Applying Artificial Neural Networks for Face Recognition

    Directory of Open Access Journals (Sweden)

    Thai Hoang Le

    2011-01-01

    Full Text Available This paper introduces some novel models for all steps of a face recognition system. In the step of face detection, we propose a hybrid model combining AdaBoost and Artificial Neural Network (ABANN to solve the process efficiently. In the next step, labeled faces detected by ABANN will be aligned by Active Shape Model and Multi Layer Perceptron. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous contours. In the feature extraction step, we describe a methodology for improving the efficiency by the association of two methods: geometric feature based method and Independent Component Analysis method. In the face matching step, we apply a model combining many Neural Networks for matching geometric features of human face. The model links many Neural Networks together, so we call it Multi Artificial Neural Network. MIT + CMU database is used for evaluating our proposed methods for face detection and alignment. Finally, the experimental results of all steps on CallTech database show the feasibility of our proposed model.

  14. Neutron spectrometry with artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Rodriguez, J.M.; Mercado S, G.A. [Universidad Autonoma de Zacatecas, A.P. 336, 98000 Zacatecas (Mexico); Iniguez de la Torre Bayo, M.P. [Universidad de Valladolid, Valladolid (Spain); Barquero, R. [Hospital Universitario Rio Hortega, Valladolid (Spain); Arteaga A, T. [Envases de Zacatecas, S.A. de C.V., Zacatecas (Mexico)]. e-mail: rvega@cantera.reduaz.mx

    2005-07-01

    An artificial neural network has been designed to obtain the neutron spectra from the Bonner spheres spectrometer's count rates. The neural network was trained using 129 neutron spectra. These include isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra from mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-bin ned to 31 energy groups using the MCNP 4C code. Re-binned spectra and UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and the respective spectrum was used as output during neural network training. After training the network was tested with the Bonner spheres count rates produced by a set of neutron spectra. This set contains data used during network training as well as data not used. Training and testing was carried out in the Mat lab program. To verify the network unfolding performance the original and unfolded spectra were compared using the {chi}{sup 2}-test and the total fluence ratios. The use of Artificial Neural Networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  15. Hair Loss Diagnosis Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ahmad Esfandiari

    2012-09-01

    Full Text Available Hair is an appendage of the skin that plays an important role in the beauty of people's face. Daily averages of 50 to 80 hairs are shed naturally. Various factors are effective in hair loss. In this paper using the eight influence attributes of gender, age, genetic factors, surgery, pregnancy, Zinc deficiency, iron deficiency, anemia and the use of cosmetics, the amount of hair loss is predicted. This work has been performed using artificial neural networks. 60 percent of the collected data was used for train, 20 percent for validation and the remaining 20 percent is used for testing the neural networks. For this, various training algorithms has been used. The result of the implementation of these algorithms has been compared. It seems that neural networks can be successful to predict hair loss.

  16. Control of a hybrid compensator in a power network by an artificial neural network

    Directory of Open Access Journals (Sweden)

    I. S. Shaw

    1998-07-01

    Full Text Available Increased interest in the elimination of distortion in electrical power networks has led to the development of various compensator topologies. The increasing cost of electrical energy necessitates the cost-effective operation of any of these topologies. This paper considers the development of an artificial neural network based controller, trained by means of the backpropagation method, that ensures the cost-effective operation of the hybrid compensator consisting of various converters and filters.

  17. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  18. Parallel Neural Network-Based Motion Controller for Autonomous Underwater Vehicles

    Institute of Scientific and Technical Information of China (English)

    GAN Yong; WANG Li-rong; WAN Lei; XU Yu-ru

    2005-01-01

    A parallel neural network-based controller (PNNC) is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV "IUV-IV" and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller's performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.

  19. Proceedings of intelligent engineering systems through artificial neural networks

    International Nuclear Information System (INIS)

    This book contains the edited versions of the technical presentation of ANNIE '91, the first international meeting on Artificial Neural Networks in Engineering. The conference covered the theory of Artificial Neural Networks and its contributions in the engineering domain and attracted researchers from twelve countries. The papers in this edited book are grouped into four categories: Artificial Neural Network Architectures; Pattern Recognition; Adaptive Control, Diagnosis and Process Monitoring; and Neuro-Engineering Systems

  20. Neural-networks-based Modelling and a Fuzzy Neural Networks Controller of MCFC

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack, and the identification structure, algorithm and modeling training process are given in detail. A fuzzy controller of MCFC stack is designed. In order to improve its online control ability, a neural network trained by the I/O data of a fuzzy controller is designed. The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online. A detailed design of the controller is given. The validity of MCFC stack modelling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations.

  1. Stability analysis of discrete-time BAM neural networks based on standard neural network models

    Institute of Scientific and Technical Information of China (English)

    ZHANG Sen-lin; LIU Mei-qin

    2005-01-01

    To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks.

  2. Decoherence and Entanglement Simulation in a Model of Quantum Neural Network Based on Quantum Dots

    Directory of Open Access Journals (Sweden)

    Altaisky Mikhail V.

    2016-01-01

    Full Text Available We present the results of the simulation of a quantum neural network based on quantum dots using numerical method of path integral calculation. In the proposed implementation of the quantum neural network using an array of single-electron quantum dots with dipole-dipole interaction, the coherence is shown to survive up to 0.1 nanosecond in time and up to the liquid nitrogen temperature of 77K.We study the quantum correlations between the quantum dots by means of calculation of the entanglement of formation in a pair of quantum dots on the GaAs based substrate with dot size of 100 ÷ 101 nanometer and interdot distance of 101 ÷ 102 nanometers order.

  3. Web Page Categorization Using Artificial Neural Networks

    CERN Document Server

    Kamruzzaman, S M

    2010-01-01

    Web page categorization is one of the challenging tasks in the world of ever increasing web technologies. There are many ways of categorization of web pages based on different approach and features. This paper proposes a new dimension in the way of categorization of web pages using artificial neural network (ANN) through extracting the features automatically. Here eight major categories of web pages have been selected for categorization; these are business & economy, education, government, entertainment, sports, news & media, job search, and science. The whole process of the proposed system is done in three successive stages. In the first stage, the features are automatically extracted through analyzing the source of the web pages. The second stage includes fixing the input values of the neural network; all the values remain between 0 and 1. The variations in those values affect the output. Finally the third stage determines the class of a certain web page out of eight predefined classes. This stage i...

  4. Transient Stability Assessment using Artificial Neural Networks

    OpenAIRE

    Krishna, S; Padiyar, KR

    2000-01-01

    Online transient stability assessment (TSA) of a power system is not yet feasible due to the intensive computation involved. Artificial neural networks (ANN) have been proposed as one of the approaches to this problem because of their ability to quickly map nonlinear relationships between the input data and the output. In this paper a review of the previously published papers on TSA using ANN is presented. The paper also reports the results of the application of ANN to the problem of TSA of a...

  5. The Stellar parametrization using Artificial Neural Network

    CERN Document Server

    Giridhar, Sunetra; Kunder, Andrea; Muneer, S; Kumar, G Selva

    2012-01-01

    An update on recent methods for automated stellar parametrization is given. We present preliminary results of the ongoing program for rapid parametrization of field stars using medium resolution spectra obtained using Vainu Bappu Telescope at VBO, Kavalur, India. We have used Artificial Neural Network for estimating temperature, gravity, metallicity and absolute magnitude of the field stars. The network for each parameter is trained independently using a large number of calibrating stars. The trained network is used for estimating atmospheric parameters of unexplored field stars.

  6. Mechanical stress in abdominal aortic aneurysms using artificial neural networks

    OpenAIRE

    Soudah Prieto, Eduardo; Rodriguez, Jose; López González, Roberto

    2015-01-01

    Combination of numerical modeling and artificial intelligence (AI) in bioengineering processes are a promising pathway for the further development of bioengineering sciences. The objective of this work is to use Artificial Neural Networks (ANN) to reduce the long computational times needed in the analysis of shear stress in the Abdominal Aortic Aneurysm (AAA) by finite element methods (FEM). For that purpose two different neural networks are created. The first neural network (Mesh Neural Netw...

  7. Neural Network-Based Model for Landslide Susceptibility and Soil Longitudinal Profile Analyses

    DEFF Research Database (Denmark)

    Farrokhzad, F.; Barari, Amin; Choobbasti, A. J.;

    2011-01-01

    The purpose of this study was to create an empirical model for assessing the landslide risk potential at Savadkouh Azad University, which is located in the rural surroundings of Savadkouh, about 5 km from the city of Pol-Sefid in northern Iran. The soil longitudinal profile of the city of Babol......, located 25 km from the Caspian Sea, also was predicted with an artificial neural network (ANN). A multilayer perceptron neural network model was applied to the landslide area and was used to analyze specific elements in the study area that contributed to previous landsliding events. The ANN models were...... studies in landslide susceptibility zonation....

  8. Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

    International Nuclear Information System (INIS)

    With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

  9. Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M. [Escuela Politecnica Superior, Departamento de Electrotecnia y Electronica, Avda. Menendez Pidal s/n, Cordoba (Spain); Martinez B, M. R.; Vega C, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Calle Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas (Mexico); Gallego D, E.; Lorente F, A. [Universidad Politecnica de Madrid, Departamento de Ingenieria Nuclear, ETSI Industriales, C. Jose Gutierrez Abascal 2, 28006 Madrid (Spain); Mendez V, R.; Los Arcos M, J. M.; Guerrero A, J. E., E-mail: morvymm@yahoo.com.m [CIEMAT, Laboratorio de Metrologia de Radiaciones Ionizantes, Avda. Complutense 22, 28040 Madrid (Spain)

    2011-02-15

    With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

  10. RRAM-based hardware implementations of artificial neural networks: progress update and challenges ahead

    Science.gov (United States)

    Prezioso, M.; Merrikh-Bayat, F.; Chakrabarti, B.; Strukov, D.

    2016-02-01

    Artificial neural networks have been receiving increasing attention due to their superior performance in many information processing tasks. Typically, scaling up the size of the network results in better performance and richer functionality. However, large neural networks are challenging to implement in software and customized hardware are generally required for their practical implementations. In this work, we will discuss our group's recent efforts on the development of such custom hardware circuits, based on hybrid CMOS/memristor circuits, in particular of CMOL variety. We will start by reviewing the basics of memristive devices and of CMOL circuits. We will then discuss our recent progress towards demonstration of hybrid circuits, focusing on the experimental and theoretical results for artificial neural networks based on crossbarintegrated metal oxide memristors. We will conclude presentation with the discussion of the remaining challenges and the most pressing research needs.

  11. Neural network based load and price forecasting and confidence interval estimation in deregulated power markets

    Science.gov (United States)

    Zhang, Li

    With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies. In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman

  12. GENERALIZED REGRESSION NEURAL NETWORK BASED EXPERT SYSTEM FOR HEPATITIS B DIAGNOSIS

    Directory of Open Access Journals (Sweden)

    C. Mahesh

    2014-01-01

    Full Text Available Hepatitis B is a potentially life-threatening liver infection caused by the hepatitis B virus. The virus interferes with the function of the liver while replicating in hepatocytes. It is a major global health problem and the most serious type of viral hepatitis. Chronic liver disease is caused by viral hepatitis and putting people at high risk of death from cirrhosis of the liver and liver cancer. Medical information available is extensive and which is utilized by the clinical specialists. The ranging of information is from details of clinical symptoms to various types of biochemical data. Information provided by each data is evaluated and assigned to a particular pathology during the diagnostic process. Artificial intelligence methods especially computer aided diagnosis and artificial neural networks can be employed to streamline the diagnostic process. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. Artificial neural networks are finding many uses in the medical diagnosis application. In this study we have proposed a Generalized Regression Neural Network (GRNN based expert system for the diagnosis of the hepatitis B virus disease. The system classifies each patient into infected and non-infected. If infected then how severe it is in terms of intensity rate.

  13. A Dynamic Effective Fault Tolerance System in Robotic Manipulator using a Hybrid Neural Network based Controller

    Directory of Open Access Journals (Sweden)

    G. Jiji

    2014-04-01

    Full Text Available Robot manipulator play important role in the field of automobile industry, mainly it is used in gas welding application and manufacturing and assembling of motor parts. In complex trajectory, on each joint the speed of the robot manipulator is affected. For that reason, it is necessary to analyze the noise and vibration of robot's joints for predicting faults also improve the control precision of robotic manipulator. In this study we will propose a new fault detection system for Robot manipulator. The proposed hybrid fault detection system is designed based on fuzzy support vector machine and Artificial Neural Networks (ANNs. In this system the decouple joints are identified and corrected using fuzzy SVM, here non-linear signal are used for complete process and treatment, the Artificial Neural Networks (ANNs are used to detect the free-swinging and locked joint of the robot, two types of neural predictors are also employed in the proposed adaptive neural network structure. The simulation results of a hybrid controller demonstrate the feasibility and performance of the methodology.

  14. Computational Ecology: Artificial Neural Networks and Their Applications

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2011-04-01

    Full Text Available A book, Computational Ecology: Artificial Neural Networks and Their Applications, published in 2010, was introduced and reviewed. This book provides readers with deep insights on algorithms, codes, and applications of artificial neural networks in ecology. A science discipline, computational ecology, is clearly defined and outlined in the book.

  15. Artificial neural networks in predicting current in electric arc furnaces

    Science.gov (United States)

    Panoiu, M.; Panoiu, C.; Iordan, A.; Ghiormez, L.

    2014-03-01

    The paper presents a study of the possibility of using artificial neural networks for the prediction of the current and the voltage of Electric Arc Furnaces. Multi-layer perceptron and radial based functions Artificial Neural Networks implemented in Matlab were used. The study is based on measured data items from an Electric Arc Furnace in an industrial plant in Romania.

  16. Neural network based system for script identification in Indian documents

    Indian Academy of Sciences (India)

    S Basavaraj Patil; N V Subbareddy

    2002-02-01

    The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. Script identification is a basic requirement in automation of document processing, in multi-script, multi-lingual environments. The system developed includes a feature extractor and a modular neural network. The feature extractor consists of two stages. In the first stage the document image is dilated using 3 × 3 masks in horizontal, vertical, right diagonal, and left diagonal directions. In the next stage, average pixel distribution is found in these resulting images. The modular network is a combination of separately trained feedforward neural network classifiers for each script. The system recognizes 64 × 64 pixel document images. In the next level, the system is modified to perform on single word-document images in the same three scripts. Modified system includes a pre-processor, modified feature extractor and probabilistic neural network classifier. Pre-processor segments the multi-script multi-lingual document into individual words. The feature extractor receives these word-document images of variable size and still produces the discriminative features employed by the probabilistic neural classifier. Experiments are conducted on a manually developed database of document images of size 64 × 64 pixels and on a database of individual words in the three scripts. The results are very encouraging and prove the effectiveness of the approach.

  17. Back propagation neural network based control for the heating system of a polysilicon reduction furnace

    Science.gov (United States)

    Cheng, Yuhua; Chen, Kai; Bai, Libing; Dai, Meizhi

    2013-12-01

    In this paper, the Back Propagation (BP) neural network based control strategy is proposed for the heating system of a polysilicon reduction furnace. It is applied to obtain the control signal Id, which is used to adjust the heating power through operations of the silicon core temperature, furnace temperature, silicon core voltage, and resistance of the current control cycle. With the control signal Id the polycrystalline silicon can be heated from room temperature to the required temperature smoothly and steadily. The proposed BP network applied in this paper can obtain the accurate control signal Id and achieve the precise control purpose. This paper presents the principle of the BP network and demonstrates the effectiveness of the BP network in the heating system of a polysilicon reduction furnace by combining the simulation analysis with experimental results.

  18. Estimation of tool wear during CNC milling using neural network-based sensor fusion

    Science.gov (United States)

    Ghosh, N.; Ravi, Y. B.; Patra, A.; Mukhopadhyay, S.; Paul, S.; Mohanty, A. R.; Chattopadhyay, A. B.

    2007-01-01

    Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations.

  19. Radial Basis Function Neural Network Based Super-Resolution Restoration for an Underspled Image

    Institute of Scientific and Technical Information of China (English)

    苏秉华; 金伟其; 牛丽红

    2004-01-01

    To achieve restoration of high frequency information for an underspled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolution method of restoration is proposed. The RBF network configuration and processing method is suitable for a high resolution restoration from an underspled low-resolution image. The soft-competition learning scheme based on the k-means algorithm is used, and can achieve higher mapping approximation accuracy without increase in the network size. Experiments showed that the proposed algorithm can achieve a super-resolution restored image from an underspled and degraded low-resolution image, and requires a shorter training time when compared with the multiplayer perception (MLP) network.

  20. Network-based H∞ synchronization control of time-delay neural networks with communication constraints

    Science.gov (United States)

    Dong, Hui; Ling, Rongyao; Zhang, Dan

    2016-03-01

    This paper is concerned with the network-based H∞ synchronization control for a class of discrete time-delay neural networks, and attention is focused on how to reduce the communication rate since the communication resource is limited. Techniques such as the measurement size reduction, signal quantization and stochastic signal transmission are introduced to achieve the above goal. An uncertain switched system model is first proposed to capture the above-networked uncertainties. Based on the switched system theory and Lyapunov stability approach, a sufficient condition is obtained such that the closed-loop synchronization system is exponentially stable in the mean-square sense with a prescribed H∞ performance level. The controller gains are determined by solving a set of linear matrix inequalities (LMIs). A numerical example is finally presented to show the effectiveness of the proposed design method.

  1. Neural network based cluster creation in the ATLAS silicon Pixel Detector

    CERN Document Server

    Andreazza, A; The ATLAS collaboration

    2013-01-01

    The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to reconstruct the location where a charged particle passed through the sensor. The resolution given by individual pixel sizes is significantly improved by using the information from the charge sharing between pixels. Such analog cluster creation techniques have been used by the ATLAS experiment for many years to obtain an excellent performance. However, in dense environments, such as those inside high-energy jets, clusters have an increased probability of merging the charge deposited by multiple particles. Recently, a neural network based algorithm which estimates both the cluster position and whether a cluster should be split has been developed for the ATLAS Pixel Detector. The algorithm significantly reduces ambiguities in the assignment of pixel detector measurement to tracks within jets and improves the position accuracy with respect to standard interpolation techniques by taking into account the 2-dimensional ...

  2. Mesh deformation based on artificial neural networks

    Science.gov (United States)

    Stadler, Domen; Kosel, Franc; Čelič, Damjan; Lipej, Andrej

    2011-09-01

    In the article a new mesh deformation algorithm based on artificial neural networks is introduced. This method is a point-to-point method, meaning that it does not use connectivity information for calculation of the mesh deformation. Two already known point-to-point methods, based on interpolation techniques, are also presented. In contrast to the two known interpolation methods, the new method does not require a summation over all boundary nodes for one displacement calculation. The consequence of this fact is a shorter computational time of mesh deformation, which is proven by different deformation tests. The quality of the deformed meshes with all three deformation methods was also compared. Finally, the generated and the deformed three-dimensional meshes were used in the computational fluid dynamics numerical analysis of a Francis water turbine. A comparison of the analysis results was made to prove the applicability of the new method in every day computation.

  3. A new approach to artificial neural networks.

    Science.gov (United States)

    Baptista Filho, B D; Cabral, E L; Soares, A J

    1998-01-01

    A novel approach to artificial neural networks is presented. The philosophy of this approach is based on two aspects: the design of task-specific networks, and a new neuron model with multiple synapses. The synapses' connective strengths are modified through selective and cumulative processes conducted by axo-axonic connections from a feedforward circuit. This new concept was applied to the position control of a planar two-link manipulator exhibiting excellent results on learning capability and generalization when compared with a conventional feedforward network. In the present paper, the example shows only a network developed from a neuronal reflexive circuit with some useful artifices, nevertheless without the intention of covering all possibilities devised.

  4. Dynamic artificial neural networks with affective systems.

    Science.gov (United States)

    Schuman, Catherine D; Birdwell, J Douglas

    2013-01-01

    Artificial neural networks (ANNs) are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP) and long term depression (LTD), and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance.

  5. Evolving A-Type Artificial Neural Networks

    CERN Document Server

    Orr, Ewan

    2011-01-01

    We investigate Turing's notion of an A-type artificial neural network. We study a refinement of Turing's original idea, motivated by work of Teuscher, Bull, Preen and Copeland. Our A-types can process binary data by accepting and outputting sequences of binary vectors; hence we can associate a function to an A-type, and we say the A-type {\\em represents} the function. There are two modes of data processing: clamped and sequential. We describe an evolutionary algorithm, involving graph-theoretic manipulations of A-types, which searches for A-types representing a given function. The algorithm uses both mutation and crossover operators. We implemented the algorithm and applied it to three benchmark tasks. We found that the algorithm performed much better than a random search. For two out of the three tasks, the algorithm with crossover performed better than a mutation-only version.

  6. Electronic Noses Using Quantitative Artificial Neural Networ

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The present paper covers a new type of electronic nose(e-nose) with a four-sensor array,which has been applied to detecting gases quantitatively in the presence of interference. This e-nose has adapted fundamental aspects of relative error(RE) in changing quantitative analysis into the artificial neural network (ANN).. Thus, both the quantitative and the qualitative requirements for ANN in implementing e-nose can be satisfied. In addition, the e-nose uses only 4 sensors in the sensor array, and can be designed for different usages simply by changing one or two sensor(s). Various gases were tested by this kind of e-nose, including alcohol vapor, CO, liquefied-petrol-gas and CO2. Satisfactory quantitative results were obtained and no qualitative mistake in prediction was observed for the samples being mixed with interference gases.

  7. Spatial predictive mapping using artificial neural networks

    Science.gov (United States)

    Noack, S.; Knobloch, A.; Etzold, S. H.; Barth, A.; Kallmeier, E.

    2014-11-01

    The modelling or prediction of complex geospatial phenomena (like formation of geo-hazards) is one of the most important tasks for geoscientists. But in practice it faces various difficulties, caused mainly by the complexity of relationships between the phenomena itself and the controlling parameters, as well by limitations of our knowledge about the nature of physical/ mathematical relationships and by restrictions regarding accuracy and availability of data. In this situation methods of artificial intelligence, like artificial neural networks (ANN) offer a meaningful alternative modelling approach compared to the exact mathematical modelling. In the past, the application of ANN technologies in geosciences was primarily limited due to difficulties to integrate it into geo-data processing algorithms. In consideration of this background, the software advangeo® was developed to provide a normal GIS user with a powerful tool to use ANNs for prediction mapping and data preparation within his standard ESRI ArcGIS environment. In many case studies, such as land use planning, geo-hazards analysis and prevention, mineral potential mapping, agriculture & forestry advangeo® has shown its capabilities and strengths. The approach is able to add considerable value to existing data.

  8. Artificial neural networks in neutron dosimetry

    International Nuclear Information System (INIS)

    An artificial neural network (ANN) has been designed to obtain neutron doses using only the count rates of a Bonner spheres spectrometer (BSS). Ambient, personal and effective neutron doses were included. One hundred and eighty-one neutron spectra were utilised to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in the BSS and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing were carried out in the MATLABR environment. The impact of uncertainties in BSS count rates upon the dose quantities calculated with the ANN was investigated by modifying by ±5% the BSS count rates used in the training set. The use of ANNs in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated with this ill-conditioned problem. (authors)

  9. Neural Network Based on Quantum Chemistry for Predicting Melting Point of Organic Compounds

    Institute of Scientific and Technical Information of China (English)

    Juan A. Lazzús

    2009-01-01

    The melting points of organic compounds were estimated using a combined method that includes a backpropagation neural network and quantitative structure property relationship (QSPR) parameters in quantum chemistry. Eleven descriptors that reflect the intermolec-ular forces and molecular symmetry were used as input variables. QSPR parameters were calculated using molecular modeling and PM3 semi-empirical molecular orbital theories. A total of 260 compounds were used to train the network, which was developed using MatLab. Then, the melting points of 73 other compounds were predicted and results were compared to experimental data from the literature. The study shows that the chosen artificial neural network and the quantitative structure property relationships method present an excellent alternative for the estimation of the melting point of an organic compound, with average absolute deviation of 5%.

  10. Online particle detection with Neural Networks based on topological calorimetry information

    International Nuclear Information System (INIS)

    This paper presents the latest results from the Ringer algorithm, which is based on artificial neural networks for the electron identification at the online filtering system of the ATLAS particle detector, in the context of the LHC experiment at CERN. The algorithm performs topological feature extraction using the ATLAS calorimetry information (energy measurements). The extracted information is presented to a neural network classifier. Studies showed that the Ringer algorithm achieves high detection efficiency, while keeping the false alarm rate low. Optimizations, guided by detailed analysis, reduced the algorithm execution time by 59%. Also, the total memory necessary to store the Ringer algorithm information represents less than 6.2 percent of the total filtering system amount.

  11. Neural Network Based on GA-BP Algorithm and its Application in the Protein Secondary Structure Prediction

    Institute of Scientific and Technical Information of China (English)

    YANG Yang; LI Kai-yang

    2006-01-01

    The advantages and disadvantages of genetic algorithm and BP algorithm are introduced. A neural network based on GA-BP algorithm is proposed and applied in the prediction of protein secondary structure, which combines the advantages of BP and GA. The prediction and training on the neural network are made respectively based on 4 structure classifications of protein so as to get higher rate of predication-the highest prediction rate 75.65%, the average prediction rate 65.04%.

  12. Modelling urban air quality using artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Nagendra, S.M. Shiva; Khare, Mukesh [Indian Institute of Technology Delhi, Department of Civil Engineering, New Delhi (India)

    2005-05-01

    This paper describes the development of artificial neural network-based vehicular exhaust emission models for predicting 8-h average carbon monoxide concentrations at two air quality control regions (AQCRs) in the city of Delhi, India, viz. a typical traffic intersection (AQCR1) and a typical arterial road (AQCR2). Maximum of ten meteorological and six traffic characteristic variables have been used in the models' formulation. Three scenarios were considered - considering both meteorological and traffic characteristics input parameters; only meteorological inputs; and only traffic characteristics input data. The performance of all the developed models was evaluated on the basis of index of agreement (d) and other statistical parameters, viz. the mean and the deviations of the observed and predicted concentrations, mean bias error, mean square error, systematic and unsystematic root mean square error, coefficient of determination and linear best fit constant and gradient (Willmott in B Am Meteorol Soc 63:1309, 1982). The forecast performance of the developed models, with meteorological and traffic characteristics (d=0.78 for AQCR1 and d=0.69 for AQCR2) and with only meteorological inputs (d=0.77 for AQCR1 and d=0.67 for AQCR2), were comparable with the measured data. (orig.)

  13. A Neural Network Based MPPT Technique Controller for Photovoltaic Pumping System

    OpenAIRE

    Mohammed Yaichi; Mohammed-Karim Fellah; Abdelkrim Mammeri

    2014-01-01

    The article proposes a novel method using the artificial neural network (ANN) for the improvement of the performances of a photovoltaic system composed of a photovoltaic (PV) array, an inverter, a motor asynchronous and a centrifugal pump. For this type of system, different optimization strategies have been proposed to improve the over of the PV system efficiency, i.e. the PV generator is forced to operate at its maximum power point “MPPT”, generally, by the insertion of DC/DC boost converter...

  14. A Neural Network Based Recognition and Classification of Commonly Used Indian Non Leafy Vegetables

    Directory of Open Access Journals (Sweden)

    Ajit Danti

    2014-09-01

    Full Text Available A methodology to characterize the commonly used Indian non-leafy vegetables’ images is developed. From the captured images of Indian non-leafy vegetables, color components, namely, RGB and HSV features are extracted, analyzed and classified. A feed forward backpropagation artificial neural network (BPNN is used for the classification. The results show that it has good robustness and a very high success rate in the range of 96-100% for eight types of vegetables. The work finds usefulness in developing recognition system for super market, automatic vending, packing and grading of vegetables, food preparation and Agriculture Produce Market Committee (APMC.

  15. Research on elastic modulus backcalculation of asphalt course using BP artificial neural network based on surface deflection basin of pavement%基于路表弯沉盆的BP人工神经网络反演沥青面层弹性模量研究

    Institute of Scientific and Technical Information of China (English)

    杨国良; 钟雯; 黄晓韵; 梁思敏; 何慧慧; 陈家驹

    2015-01-01

    Based on layered elastic theory,the elastic modulus of asphalt course in asphalt pavement was predicted using BP artificial neural network.According to the types of pavement structure in common use,the database of surface deflections with their corresponding structural parameters of asphalt course based on layered elastic theory was established.The elastic modulus backcalculation model of asphalt course in asphalt pavement was developed using BP artificial neural network to predict.The predictive results of asphalt course elastic modulus backcalculation using theoretical deflection basin and measured deflection basin indicate that the elastic modulus backcalculation model of asphalt course in asphalt pavement is of good predictive accuracy and reliability.It would provide the references with the elastic modulus backcalculation model of asphalt course to accurately and quickly estimate the conditions of asphalt course in asphalt pavement.%基于层状弹性体系理论,建立BP人工神经网络反演沥青路面沥青面层弹性模量预测模型,利用BP人工神经网络预测沥青路面沥青面层弹性模量.理论弯沉盆和实测弯沉盆反演沥青面层弹性模量的结果表明,建立的BP人工神经网络反演沥青路面沥青面层弹性模量模型具有良好的预测精度和可靠性,为评价沥青路面的沥青面层性能状况提供了参考.

  16. Liquefaction Microzonation of Babol City Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, F.; Choobbasti, A.J.; Barari, Amin

    2012-01-01

    that will be less susceptible to damage during earthquakes. The scope of present study is to prepare the liquefaction microzonation map for the Babol city based on Seed and Idriss (1983) method using artificial neural network. Artificial neural network (ANN) is one of the artificial intelligence (AI) approaches...... is proposed in this paper. To meet this objective, an effort is made to introduce a total of 30 boreholes data in an area of 7 km2 which includes the results of field tests into the neural network model and the prediction of artificial neural network is checked in some test boreholes, finally the liquefaction...... that can be classified as machine learning. Simplified methods have been practiced by researchers to assess nonlinear liquefaction potential of soil. In order to address the collective knowledge built-up in conventional liquefaction engineering, an alternative general regression neural network model...

  17. Neural network-based method for intrathoracic airway detection from three-dimensional CT images

    International Nuclear Information System (INIS)

    This paper presents a neural network-based method for intrathoracic airway detection and segmentation from three-dimensional HRCT images. Two feed-forward neural networks are independently trained to identify various airway appearances in 3-dimensional CT images. While the first network identifies potential airways located adjacent to vessels, the second network identifies potential airways by assessing the existence of walls surrounding airways. The two networks are combined to construct a dual-network classifier taking its inputs from a 21 x 21 moving subimage window: (1) raw gray-level subimage and (2) 4 directional profiles. By design, each network provides a superset of airways that are present in the CT images and only the airways identified by both networks are considered reliable. After the networks are trained by the generalized delta rule with momentum using a limited number of airway/non-airway samples apart from the validation data sets, the generalization performance of the networks is assessed with two independent standards consisting of 282 and 167 observer traced airways. The performance of the current method is compared with that of the conventional seeded region growing method. The validation results indicate that the presented method indeed provide enhanced detection of peripheral airways compared to the conventional region growing method

  18. Artificial Neural Network applied to lightning flashes

    Science.gov (United States)

    Gin, R. B.; Guedes, D.; Bianchi, R.

    2013-05-01

    The development of video cameras enabled cientists to study lightning discharges comportment with more precision. The main goal of this project is to create a system able to detect images of lightning discharges stored in videos and classify them using an Artificial Neural Network (ANN)using C Language and OpenCV libraries. The developed system, can be split in two different modules: detection module and classification module. The detection module uses OpenCV`s computer vision libraries and image processing techniques to detect if there are significant differences between frames in a sequence, indicating that something, still not classified, occurred. Whenever there is a significant difference between two consecutive frames, two main algorithms are used to analyze the frame image: brightness and shape algorithms. These algorithms detect both shape and brightness of the event, removing irrelevant events like birds, as well as detecting the relevant events exact position, allowing the system to track it over time. The classification module uses a neural network to classify the relevant events as horizontal or vertical lightning, save the event`s images and calculates his number of discharges. The Neural Network was implemented using the backpropagation algorithm, and was trained with 42 training images , containing 57 lightning events (one image can have more than one lightning). TheANN was tested with one to five hidden layers, with up to 50 neurons each. The best configuration achieved a success rate of 95%, with one layer containing 20 neurons (33 test images with 42 events were used in this phase). This configuration was implemented in the developed system to analyze 20 video files, containing 63 lightning discharges previously manually detected. Results showed that all the lightning discharges were detected, many irrelevant events were unconsidered, and the event's number of discharges was correctly computed. The neural network used in this project achieved a

  19. A study on precursors leading to geomagnetic storms using artificial neural network

    Science.gov (United States)

    Singh, Gaurav; Singh, A. K.

    2016-06-01

    Space weather prediction involves advance forecasting of the magnitude and onset time of major geomagnetic storms on Earth. In this paper, we discuss the development of an artificial neural network-based model to study the precursor leading to intense and moderate geomagnetic storms, following halo coronal mass ejection (CME) and related interplanetary (IP) events. IP inputs were considered within a 5-day time window after the commencement of storm. The artificial neural network (ANN) model training, testing and validation datasets were constructed based on 110 halo CMEs (both full and partial halo and their properties) observed during the ascending phase of the 24th solar cycle between 2009 and 2014. The geomagnetic storm occurrence rate from halo CMEs is estimated at a probability of 79%, by this model.

  20. A study on precursors leading to geomagnetic storms using artificial neural network

    Science.gov (United States)

    Singh, Gaurav; Singh, A. K.

    2016-07-01

    Space weather prediction involves advance forecasting of the magnitude and onset time of major geomagnetic storms on Earth. In this paper, we discuss the development of an artificial neural network-based model to study the precursor leading to intense and moderate geomagnetic storms, following halo coronal mass ejection (CME) and related interplanetary (IP) events. IP inputs were considered within a 5-day time window after the commencement of storm. The artificial neural network (ANN) model training, testing and validation datasets were constructed based on 110 halo CMEs (both full and partial halo and their properties) observed during the ascending phase of the 24th solar cycle between 2009 and 2014. The geomagnetic storm occurrence rate from halo CMEs is estimated at a probability of 79%, by this model.

  1. Neural network based prediction of roughing and finishing times in a hot strip mill

    Directory of Open Access Journals (Sweden)

    Colla, V.

    2010-02-01

    Full Text Available The paper presents a model based on neural networks which is able to predict the time required to pass the different gauges of a roughing and finishing mill as function of some slab features and process parameters. The final aim of the work is to increase the rolling efficiency while avoiding collisions and queues that cause time and energy losses. Neural networks are suitable to this prediction task as they are particularly able to cope with unknown non linear relationships between input and output variables. Moreover they can learn from real industrial data and therefore do not require prior assumptions or mathematical modelling of the process and transferability is ensured by the possibility to use different databases coming from different rolling mills. In the paper, two different kinds of neural network- based models have been proposed, their performances have been discussed and compared.

    En este artículo se presenta un modelo basado en redes neuronales capaz de predecir el tiempo necesario para pasar las diferentes galgas de un tren desbastador y acabador en función de ciertas características del desbaste y parámetros de proceso. El objetivo final es aumentar la eficacia de la laminación evitando colisiones y colas que provoquen pérdidas de tiempo y energía. Se propone utilizar para esta tarea redes neuronales por su capacidad de predicción en aquellos casos en los que existen relaciones no lineales desconocidas entre las variables de entrada y las de salida. Además, son capaces de aprender de datos industriales reales y, por tanto, no requieren suposiciones previas o modelos matemáticos del proceso, estando la transferibilidad asegurada ya que es posible utilizar distintas bases de datos procedentes de diferentes trenes de laminación.

  2. A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks

    Directory of Open Access Journals (Sweden)

    Nenad Kojić

    2012-06-01

    Full Text Available The networking infrastructure of wireless mesh networks (WMNs is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs. This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission. The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.

  3. A neural networks-based hybrid routing protocol for wireless mesh networks.

    Science.gov (United States)

    Kojić, Nenad; Reljin, Irini; Reljin, Branimir

    2012-01-01

    The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic-i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance. PMID:22969360

  4. Forecasting Zakat collection using artificial neural network

    Science.gov (United States)

    Sy Ahmad Ubaidillah, Sh. Hafizah; Sallehuddin, Roselina

    2013-04-01

    'Zakat', "that which purifies" or "alms", is the giving of a fixed portion of one's wealth to charity, generally to the poor and needy. It is one of the five pillars of Islam, and must be paid by all practicing Muslims who have the financial means (nisab). 'Nisab' is the minimum level to determine whether there is a 'zakat' to be paid on the assets. Today, in most Muslim countries, 'zakat' is collected through a decentralized and voluntary system. Under this voluntary system, 'zakat' committees are established, which are tasked with the collection and distribution of 'zakat' funds. 'Zakat' promotes a more equitable redistribution of wealth, and fosters a sense of solidarity amongst members of the 'Ummah'. The Malaysian government has established a 'zakat' center at every state to facilitate the management of 'zakat'. The center has to have a good 'zakat' management system to effectively execute its functions especially in the collection and distribution of 'zakat'. Therefore, a good forecasting model is needed. The purpose of this study is to develop a forecasting model for Pusat Zakat Pahang (PZP) to predict the total amount of collection from 'zakat' of assets more precisely. In this study, two different Artificial Neural Network (ANN) models using two different learning algorithms are developed; Back Propagation (BP) and Levenberg-Marquardt (LM). Both models are developed and compared in terms of their accuracy performance. The best model is determined based on the lowest mean square error and the highest correlations values. Based on the results obtained from the study, BP neural network is recommended as the forecasting model to forecast the collection from 'zakat' of assets for PZP.

  5. Advances in Artificial Neural Networks – Methodological Development and Application

    Directory of Open Access Journals (Sweden)

    Yanbo Huang

    2009-08-01

    Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological

  6. Particle Swarm Optimization Recurrent Neural Network Based Z-source Inverter Fed Induction Motor Drive

    Directory of Open Access Journals (Sweden)

    R. Selva Santhose Kumar

    2014-06-01

    Full Text Available In this study, the proposal is made for Particle Swarm Optimization (PSO Recurrent Neural Network (RNN based Z-Source Inverter Fed Induction Motor Drive. The proposed method is used to enhance the performance of the induction motor while reducing the Total Harmonic Distortion (THD, eliminating the oscillation period of the stator current, torque and speed. Here, the PSO technique uses the induction motor speed and reference speed as the input parameters. From the input parameters, it optimizes the gain of the PI controller and generates the reference quadrature axis current. By using the RNN, the reference three phase current for accurate control pulses of the voltage source inverter is predicted. The RNN is trained by the input motor actual quadrature axis current and the reference quadrature axis current with the corresponding target reference three phase current. The training process utilized the supervised learning process. Then the proposed technique is implemented in the MATLAB/SIMULINK platform and the effectiveness is analyzed by comparing with the other techniques such as PSO-Radial Biased Neural Network (RBNN and PSO-Artificial Neural Network (ANN. The comparison results demonstrate the superiority of the proposed approach and confirm its potential to solve the problem.

  7. Detection of Wildfires with Artificial Neural Networks

    Science.gov (United States)

    Umphlett, B.; Leeman, J.; Morrissey, M. L.

    2011-12-01

    Currently fire detection for the National Oceanic and Atmospheric Administration (NOAA) using satellite data is accomplished with algorithms and error checking human analysts. Artificial neural networks (ANNs) have been shown to be more accurate than algorithms or statistical methods for applications dealing with multiple datasets of complex observed data in the natural sciences. ANNs also deal well with multiple data sources that are not all equally reliable or equally informative to the problem. An ANN was tested to evaluate its accuracy in detecting wildfires utilizing polar orbiter numerical data from the Advanced Very High Resolution Radiometer (AVHRR). Datasets containing locations of known fires were gathered from the NOAA's polar orbiting satellites via the Comprehensive Large Array-data Stewardship System (CLASS). The data was then calibrated and navigation corrected using the Environment for Visualizing Images (ENVI). Fires were located with the aid of shapefiles generated via ArcGIS. Afterwards, several smaller ten pixel by ten pixel datasets were created for each fire (using the ENVI corrected data). Several datasets were created for each fire in order to vary fire position and avoid training the ANN to look only at fires in the center of an image. Datasets containing no fires were also created. A basic pattern recognition neural network was established with the MATLAB neural network toolbox. The datasets were then randomly separated into categories used to train, validate, and test the ANN. To prevent over fitting of the data, the mean squared error (MSE) of the network was monitored and training was stopped when the MSE began to rise. Networks were tested using each channel of the AVHRR data independently, channels 3a and 3b combined, and all six channels. The number of hidden neurons for each input set was also varied between 5-350 in steps of 5 neurons. Each configuration was run 10 times, totaling about 4,200 individual network evaluations. Thirty

  8. DEM interpolation based on artificial neural networks

    Science.gov (United States)

    Jiao, Limin; Liu, Yaolin

    2005-10-01

    This paper proposed a systemic resolution scheme of Digital Elevation model (DEM) interpolation based on Artificial Neural Networks (ANNs). In this paper, we employ BP network to fit terrain surface, and then detect and eliminate the samples with gross errors. This paper uses Self-organizing Feature Map (SOFM) to cluster elevation samples. The study area is divided into many more homogenous tiles after clustering. BP model is employed to interpolate DEM in each cluster. Because error samples are eliminated and clusters are built, interpolation result is better. The case study indicates that ANN interpolation scheme is feasible. It also shows that ANN can get a more accurate result by comparing ANN with polynomial and spline interpolation. ANN interpolation doesn't need to determine the interpolation function beforehand, so manmade influence is lessened. The ANN interpolation is more automatic and intelligent. At the end of the paper, we propose the idea of constructing ANN surface model. This model can be used in multi-scale DEM visualization, and DEM generalization, etc.

  9. Artificial neural network models for image understanding

    Science.gov (United States)

    Kulkarni, Arun D.; Byars, P.

    1991-06-01

    In this paper we introduce a new class of artificial neural network (ANN) models based on transformed domain feature extraction. Many optical and/or digital recognition systems based on transformed domain feature extraction are available in practice. Optical systems are inherently parallel in nature and are preferred for real time applications, whereas digital systems are more suitable for nonlinear operations. In our ANN models we combine advantages of both digital and optical systems. Many transformed domain feature extraction techniques have been developed during the last three decades. They include: the Fourier transform (FT), the Walsh Hadamard transform (WHT), the discrete cosine transform (DCT), etc. As an example, we have developed ANN models using the FT and WHT domain features. The models consist of two stages, the feature extraction stage and the recognition stage. We have used back-propagation and competitive learning algorithms in the recognition stage. We have used these ANN models for invariant object recognition. The models have been used successfully to recognize various types of aircraft, and also have been tested with test patterns. ANN models based on other transforms can be developed in a similar fashion.

  10. Groundwater Level Predictions Using Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    毛晓敏; 尚松浩; 刘翔

    2002-01-01

    The prediction of groundwater level is important for the use and management of groundwater resources. In this paper, the artificial neural networks (ANN) were used to predict groundwater level in the Dawu Aquifer of Zibo in Eastern China. The first step was an auto-correlation analysis of the groundwater level which showed that the monthly groundwater level was time dependent. An auto-regression type ANN (ARANN) model and a regression-auto-regression type ANN (RARANN) model using back-propagation algorithm were then used to predict the groundwater level. Monthly data from June 1988 to May 1998 was used for the network training and testing. The results show that the RARANN model is more reliable than the ARANN model, especially in the testing period, which indicates that the RARANN model can describe the relationship between the groundwater fluctuation and main factors that currently influence the groundwater level. The results suggest that the model is suitable for predicting groundwater level fluctuations in this area for similar conditions in the future.

  11. Artificial neural network for violation analysis

    International Nuclear Information System (INIS)

    Barrier removal (BR) is a safety-related violation, and it can be analyzed in terms of benefits, costs, and potential deficits. In order to allow designers to integrate BR into the risk analysis during the initial design phase or during re-design work, we propose a connectionist method integrating self-organizing maps (SOM). The basic SOM is an artificial neural network that, on the basis of the information contained in a multi-dimensional space, generates a space of lesser dimensions. Three algorithms--Unsupervised SOM, Supervised SOM, and Hierarchical SOM--have been developed to permit BR classification and prediction in terms of the different criteria. The proposed method can be used, on the one hand, to foresee/predict the possibility level of a new/changed barrier (prospective analysis), and on the other hand, to synthetically regroup/rearrange the BR of a given human-machine system (retrospective analysis). We applied this method to the BR analysis of an experimental railway simulator, and our preliminary results are presented here

  12. Hurst Parameter Estimation Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    S..Ledesma-Orozco

    2011-08-01

    Full Text Available The Hurst parameter captures the amount of long-range dependence (LRD in a time series. There are severalmethods to estimate the Hurst parameter, being the most popular: the variance-time plot, the R/S plot, theperiodogram, and Whittle’s estimator. The first three are graphical methods, and the estimation accuracy depends onhow the plot is interpreted and calculated. In contrast, Whittle’s estimator is based on a maximum likelihood techniqueand does not depend on a graph reading; however, it is computationally expensive. A new method to estimate theHurst parameter is proposed. This new method is based on an artificial neural network. Experimental results showthat this method outperforms traditional approaches, and can be used on applications where a fast and accurateestimate of the Hurst parameter is required, i.e., computer network traffic control. Additionally, the Hurst parameterwas computed on series of different length using several methods. The simulation results show that the proposedmethod is at least ten times faster than traditional methods.

  13. Electronic circuits modeling using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Andrejević Miona V.

    2003-01-01

    Full Text Available In this paper artificial neural networks (ANN are applied to modeling of electronic circuits. ANNs are used for application of the black-box modeling concept in the time domain. Modeling process is described, so the topology of the ANN, the testing signal used for excitation, together with the complexity of ANN are considered. The procedure is first exemplified in modeling of resistive circuits. MOS transistor, as a four-terminal device, is modeled. Then nonlinear negative resistive characteristic is modeled in order to be used as a piece-wise linear resistor in Chua's circuit. Examples of modeling nonlinear dynamic circuits are given encompassing a variety of modeling problems. A nonlinear circuit containing quartz oscillator is considered for modeling. Verification of the concept is performed by verifying the ability of the model to generalize i.e. to create acceptable responses to excitations not used during training. Implementation of these models within a behavioral simulator is exemplified. Every model is implemented in realistic surrounding in order to show its interaction, and of course, its usage and purpose.

  14. Doubly stochastic Poisson processes in artificial neural learning.

    Science.gov (United States)

    Card, H C

    1998-01-01

    This paper investigates neuron activation statistics in artificial neural networks employing stochastic arithmetic. It is shown that a doubly stochastic Poisson process is an appropriate model for the signals in these circuits.

  15. Layered learning of soccer robot based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Discusses the application of artificial neural network for MIROSOT, introduces a layered model of BP network of soccer robot for learning basic behavior and cooperative behavior, and concludes from experimental results that the model is effective.

  16. Geochemical characterization of oceanic basalts using artificial neural network

    Digital Repository Service at National Institute of Oceanography (India)

    Das, P.; Iyer, S.D.

    method is specifically needed to identify the OFB as normal (N-MORB), enriched (E-MORB) and ocean island basalts (OIB). Artificial Neural Network (ANN) technique as a supervised Learning Vector Quantisation (LVQ) is applied to identify the inherent...

  17. Proton exchange membrane fuel cells modeling based on artificial neural networks

    Institute of Scientific and Technical Information of China (English)

    Yudong Tian; Xinjian Zhu; Guangyi Cao

    2005-01-01

    To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.

  18. Transient stability Assessment using Artificial Neural Network Considering Fault Location

    OpenAIRE

    P.K.Olulope; Folly, K. A.; Chowdhury, S.; Chowdhury, S. P.

    2010-01-01

    This paper describes the capability of artificial neural network for predicting the critical clearing time of power system. It combines the advantages of time domain integration schemes with artificial neural network for real time transient stability assessment. The training of ANN is done using selected features as input and critical fault clearing time (CCT) as desire target. A single contingency was applied and the target CCT was found using time domain simulatio...

  19. Application of Artificial Neural Networks to Contraception Study

    Institute of Scientific and Technical Information of China (English)

    周利锋; 高尔生; 金丕焕

    1998-01-01

    As a newly developed border line science, the artificial neural network (ANN)has been applied in many fields. The ANN was used in the selection of contraceptives in the article, and the performances of the artificial neural networks and traditional multivariate logistic regression analysis method were compared with the training data and the testing data by receiver operating characteristic (ROC) curves. The results imply that ANN may be applied and developed further in statistics and medical fields hopefully.

  20. A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels

    Directory of Open Access Journals (Sweden)

    Uttam Kumar

    2012-09-01

    Full Text Available Signals acquired by sensors in the real world are non-linear combinations, requiring non-linear mixture models to describe the resultant mixture spectra for the endmember’s (pure pixel’s distribution. This communication discusses inferring class fraction through a novel hybrid mixture model (HMM. HMM is a three-step process, where the endmembers are first derived from the images themselves using the N-FINDR algorithm. These endmembers are used by the linear mixture model (LMM in the second step that provides an abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual ground proportions are fed into neural network based multi-layer perceptron (MLP architecture as input to train the neurons. The neural output further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. HMM is first implemented and validated on simulated hyper spectral data of 200 bands and subsequently on real time MODIS data with a spatial resolution of 250 m. The results on computer simulated data show that the method gives acceptable results for unmixing pixels with an overall RMSE of 0.0089 ± 0.0022 with LMM and 0.0030 ± 0.0001 with the HMM when compared to actual class proportions. The unmixed MODIS images showed overall RMSE with HMM as 0.0191 ± 0.022 as compared to the LMM output considered alone that had an overall RMSE of 0.2005 ± 0.41, indicating that individual class abundances obtained from HMM are very close to the real observations.

  1. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

    Science.gov (United States)

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2015-09-01

    Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

  2. A neural network-based exploratory learning and motor planning system for co-robots.

    Science.gov (United States)

    Galbraith, Byron V; Guenther, Frank H; Versace, Massimiliano

    2015-01-01

    Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.

  3. A neural network-based exploratory learning and motor planning system for co-robots

    Directory of Open Access Journals (Sweden)

    Byron V Galbraith

    2015-07-01

    Full Text Available Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or learning by doing, an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.

  4. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

    Science.gov (United States)

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2015-09-01

    Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids. PMID:25532191

  5. A Novel Neural Network-Based Technique for Smart Gas Sensors Operating in a Dynamic Environment

    Directory of Open Access Journals (Sweden)

    Zohir Dibi

    2009-11-01

    Full Text Available Thanks to their high sensitivity and low-cost, metal oxide gas sensors (MOX are widely used in gas detection, although they present well-known problems (lack of selectivity and environmental effects…. We present in this paper a novel neural network- based technique to remedy these problems. The idea is to create intelligent models; the first one, called corrector, can automatically linearize a sensor’s response characteristics and eliminate its dependency on the environmental parameters. The corrector’s responses are processed with the second intelligent model which has the role of discriminating exactly the detected gas (nature and concentration. The gas sensors used are industrial resistive kind (TGS8xx, by Figaro Engineering. The MATLAB environment is used during the design phase and optimization. The sensor models, the corrector, and the selective model were implemented and tested in the PSPICE simulator. The sensor model accurately expresses the nonlinear character of the response and the dependence on temperature and relative humidity in addition to their gas nature dependency. The corrector linearizes and compensates the sensor’s responses. The method discriminates qualitatively and quantitatively between seven gases. The advantage of the method is that it uses a small representative database so we can easily implement the model in an electrical simulator. This method can be extended to other sensors.

  6. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  7. Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment

    Directory of Open Access Journals (Sweden)

    Chaudhari Narendra S

    2005-01-01

    Full Text Available We present an artificial neural-network- (NN- based smart interface framework for sensors operating in harsh environments. The NN-based sensor can automatically compensate for the nonlinear response characteristics and its nonlinear dependency on the environmental parameters, with high accuracy. To show the potential of the proposed NN-based framework, we provide results of a smart capacitive pressure sensor (CPS operating in a wide temperature range of 0 to . Through simulated experiments, we have shown that the NN-based CPS model is capable of providing pressure readout with a maximum full-scale (FS error of only over this temperature range. A novel scheme for estimating the ambient temperature from the sensor characteristics itself is proposed. For this purpose, a second NN is utilized to estimate the ambient temperature accurately from the knowledge of the offset capacitance of the CPS. A microcontroller-unit- (MCU- based implementation scheme is also provided.

  8. Multiple image sensor data fusion through artificial neural networks

    Science.gov (United States)

    With multisensor data fusion technology, the data from multiple sensors are fused in order to make a more accurate estimation of the environment through measurement, processing and analysis. Artificial neural networks are the computational models that mimic biological neural networks. With high per...

  9. Optimal Brain Surgeon on Artificial Neural Networks in

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine;

    2012-01-01

    It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...

  10. THE ARTIFICIAL NEURAL NETWORK OF FORECASTING OPEN MINING SLOPE STABILITY

    Institute of Scientific and Technical Information of China (English)

    魏春启; 白润才

    2000-01-01

    The artificial neural network model which forecasts Open Mining Slope stability is established by neural network theory and method. The nonlinear reflection relation between stability target of open mining slope and its influence factor is described. The method of forecasting Open Mining Slope stability is brought forward.

  11. Development and Evolution of Neural Networks in an Artificial Chemistry

    OpenAIRE

    Astor, Jens C.; Adami, Christoph

    1998-01-01

    We present a model of decentralized growth for Artificial Neural Networks (ANNs) inspired by the development and the physiology of real nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates modeled by a simple artificial chemistry. Gene expression is manifested as axon and dendrite growth, cell division and differentiation, substrate production and c...

  12. Segmentation of magnetic resonance images using an artificial neural network.

    OpenAIRE

    Piraino, D. W.; Amartur, S. C.; Richmond, B. J.; Schils, J. P.; Thome, J. M.; Weber, P. B.

    1991-01-01

    Signal intensities from intermediate and T2 weighted spin echo images of the brain were used as inputs into an artificial neural network (ANN). The signal intensities were used to train the network to recognize anatomically-important segments. The ANN was a self-organizing map (SOM) neural network which develops a continuous topographical map of the signal intensities within the two images. The neural network segmented images demonstrated good correlation with white matter, gray matter, and c...

  13. A spiking neural network based cortex-like mechanism and application to facial expression recognition.

    Science.gov (United States)

    Fu, Si-Yao; Yang, Guo-Sheng; Kuai, Xin-Kai

    2012-01-01

    In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism. PMID:23193391

  14. A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

    Directory of Open Access Journals (Sweden)

    Si-Yao Fu

    2012-01-01

    Full Text Available In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs. By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people’s facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism.

  15. Impulsive Neural Networks Algorithm Based on the Artificial Genome Model

    Directory of Open Access Journals (Sweden)

    Yuan Gao

    2014-05-01

    Full Text Available To describe gene regulatory networks, this article takes the framework of the artificial genome model and proposes impulsive neural networks algorithm based on the artificial genome model. Firstly, the gene expression and the cell division tree are applied to generate spiking neurons with specific attributes, neural network structure, connection weights and specific learning rules of each neuron. Next, the gene segment duplications and divergence model are applied to design the evolutionary algorithm of impulsive neural networks at the level of the artificial genome. The dynamic changes of developmental gene regulatory networks are controlled during the whole evolutionary process. Finally, the behavior of collecting food for autonomous intelligent agent is simulated, which is driven by nerves. Experimental results demonstrate that the algorithm in this article has the evolutionary ability on large-scale impulsive neural networks

  16. ECO INVESTMENT PROJECT MANAGEMENT THROUGH TIME APPLYING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Tamara Gvozdenović

    2007-06-01

    Full Text Available he concept of project management expresses an indispensable approach to investment projects. Time is often the most important factor in these projects. The artificial neural network is the paradigm of data processing, which is inspired by the one used by the biological brain, and it is used in numerous, different fields, among which is the project management. This research is oriented to application of artificial neural networks in managing time of investment project. The artificial neural networks are used to define the optimistic, the most probable and the pessimistic time in PERT method. The program package Matlab: Neural Network Toolbox is used in data simulation. The feed-forward back propagation network is chosen.

  17. Indoor Positioning System Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Hamid Mehmood

    2010-01-01

    Full Text Available Problem statement: Location knowledge in indoor environment using Indoor Positioning Systems (IPS has become very useful and popular in recent years. A number of Location Based Services (LBS have been developed, which are based on IPS, these LBS include asset tracking, inventory management and security based applications. Many next-generation LBS applications such as social networking, local search, advertising and geo-tagging are expected to be used in urban and indoor environments where GNSS either underperforms in terms of fix times or accuracy, or fails altogether. To develop an IPS based on Wi-Fi Received Signal Strength (RSS using Artificial Neural Networks (ANN, which should use already available Wi-Fi infrastructure in a heterogeneous environment. Approach: This study discussed the use of ANN for IPS using RSS in an indoor wireless facility which has varying human activity, material of walls and type of Wireless Access Points (WAP, hence simulating a heterogeneous environment. The proposed system used backpropogation method with 4 input neurons, 2 output neurons and 4 hidden layers. The model was trained with three different types of training data. The accuracy assessment for each training data was performed by computing the distance error and average distance error. Results: The results of the experiments showed that using ANN with the proposed method of collecting training data, maximum accuracy of 0.7 m can be achieved, with 30% of the distance error less than 1 m and 60% of the distance error within the range of 1-2 m. Whereas maximum accuracy of 1.01 can be achieved with the commonly used method of collecting training data. The proposed model also showed 67% more accuracy as compared to a probabilistic model. Conclusion: The results indicated that ANN based IPS can provide accuracy and precision which is quite adequate for the development of indoor LBS while using the already available Wi-Fi infrastructure, also the proposed method

  18. Term Structure of Interest Rates Based on Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In light of the nonlinear approaching capability of artificial neural networks ( ANN), the term structure of interest rates is predicted using The generalized regression neural network (GRNN) and back propagation (BP) neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek's model and Cox-Ingersoll-Ross (CIR) model. The comparison reveals that neural network models outperform Vasicek's model and CIR model,which are more precise and closer to the real market situation.

  19. Adaptive Neurons For Artificial Neural Networks

    Science.gov (United States)

    Tawel, Raoul

    1990-01-01

    Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

  20. A Neural Network Based Intelligent Predictive Sensor for Cloudiness, Solar Radiation and Air Temperature

    Directory of Open Access Journals (Sweden)

    Pedro M. Ferreira

    2012-11-01

    Full Text Available Accurate measurements of global solar radiation and atmospheric temperature,as well as the availability of the predictions of their evolution over time, are importantfor different areas of applications, such as agriculture, renewable energy and energymanagement, or thermal comfort in buildings. For this reason, an intelligent, light-weightand portable sensor was developed, using artificial neural network models as the time-seriespredictor mechanisms. These have been identified with the aid of a procedure based on themulti-objective genetic algorithm. As cloudiness is the most significant factor affecting thesolar radiation reaching a particular location on the Earth surface, it has great impact on theperformance of predictive solar radiation models for that location. This work also representsone step towards the improvement of such models by using ground-to-sky hemisphericalcolour digital images as a means to estimate cloudiness by the fraction of visible skycorresponding to clouds and to clear sky. The implementation of predictive models inthe prototype has been validated and the system is able to function reliably, providingmeasurements and four-hour forecasts of cloudiness, solar radiation and air temperature.

  1. A Neural Network Based MPPT Technique Controller for Photovoltaic Pumping System

    Directory of Open Access Journals (Sweden)

    Mohammed Yaichi

    2014-03-01

    Full Text Available The article proposes a novel method using the artificial neural network (ANN for the improvement of the performances of a photovoltaic system composed of a photovoltaic (PV array, an inverter, a motor asynchronous and a centrifugal pump. For this type of system, different optimization strategies have been proposed to improve the over of the PV system efficiency, i.e. the PV generator is forced to operate at its maximum power point “MPPT”, generally, by the insertion of DC/DC boost converter between the photovoltaic array and the inverter. In this work we propose an approach, where optimization is realized without need adding a DC/DC converter to the chain, using field-oriented control through the monitoring of the voltage-fed inverter frequency. The motor is also ensured in all insolation conditions. A multilayer feed forward perception type NN is proposed for MPPT control, and the back-propagation algorithm is used for training. The performances of the drive with ANN-based MPPT are excellent. The maximum power point (MPP can be easily obtained to frequency-controlled drive.

  2. Neural Network Based Retrieval of Atmospheric Temperature Profile Using AMSU-A Observations

    Directory of Open Access Journals (Sweden)

    R. K. Gangwar

    2014-01-01

    Full Text Available The present study describes artificial neural network (ANN based approach for the retrieval of atmospheric temperature profiles from AMSU-A microwave temperature sounder. The nonlinear relationship between the temperature profiles and satellite brightness temperatures dictates the use of ANN, which is inherently nonlinear in nature. Since latitudinal variation of temperature is dominant one in the Earth’s atmosphere, separate network configurations have been established for different latitudinal belts, namely, tropics, mid-latitudes, and polar regions. Moreover, as surface emissivity in the microwave region of electromagnetic spectrum significantly influences the radiance (or equivalently the brightness temperature at the satellite altitude, separate algorithms have been developed for land and ocean for training the networks. Temperature profiles from National Center for Environmental Prediction (NCEP analysis and brightness temperature observations of AMSU-A onboard NOAA-19 for the year 2010 have been used for training of the networks. Further, the algorithm has been tested on the independent dataset comprising several months of 2012 AMSU-A observations. Finally, an error analysis has been performed by comparing retrieved profiles with collocated temperature profiles from NCEP. Errors in the tropical region are found to be less than those in the mid-latitude and polar regions. Also, in each region the errors over ocean are less than the corresponding ones over land.

  3. Neural Network Based State of Health Diagnostics for an Automated Radioxenon Sampler/Analyzer

    Energy Technology Data Exchange (ETDEWEB)

    Keller, Paul E.; Kangas, Lars J.; Hayes, James C.; Schrom, Brian T.; Suarez, Reynold; Hubbard, Charles W.; Heimbigner, Tom R.; McIntyre, Justin I.

    2009-05-13

    Artificial neural networks (ANNs) are used to determine the state-of-health (SOH) of the Automated Radioxenon Analyzer/Sampler (ARSA). ARSA is a gas collection and analysis system used for non-proliferation monitoring in detecting radioxenon released during nuclear tests. SOH diagnostics are important for automated, unmanned sensing systems so that remote detection and identification of problems can be made without onsite staff. Both recurrent and feed-forward ANNs are presented. The recurrent ANN is trained to predict sensor values based on current valve states, which control air flow, so that with only valve states the normal SOH sensor values can be predicted. Deviation between modeled value and actual is an indication of a potential problem. The feed-forward ANN acts as a nonlinear version of principal components analysis (PCA) and is trained to replicate the normal SOH sensor values. Because of ARSA’s complexity, this nonlinear PCA is better able to capture the relationships among the sensors than standard linear PCA and is applicable to both sensor validation and recognizing off-normal operating conditions. Both models provide valuable information to detect impending malfunctions before they occur to avoid unscheduled shutdown. Finally, the ability of ANN methods to predict the system state is presented.

  4. A Pattern Construction Scheme for Neural Network-Based Cognitive Communication

    Directory of Open Access Journals (Sweden)

    Ozgur Orcay

    2011-01-01

    Full Text Available Inefficient utilization of the frequency spectrum due to conventional regulatory limitations and physical performance limiting factors, mainly the Signal to Noise Ratio (SNR, are prominent restrictions in digital wireless communication. Pattern Based Communication System (PBCS is an adaptive and perceptual communication method based on a Cognitive Radio (CR approach. It intends an SNR oriented cognition mechanism in the physical layer for improvement of Link Spectral Efficiency (LSE. The key to this system is construction of optimal communication signals, which consist of encoded data in different pattern forms (waveforms depending on spectral availabilities. The signals distorted in the communication medium are recovered according to the pre-trained pattern glossary by the perceptual receiver. In this study, we have shown that it is possible to improve the bandwidth efficiency when largely uncorrelated signal patterns are chosen in order to form a glossary that represents symbols for different length data groups and the information can be recovered by the Artificial Neural Network (ANN in the receiver site.

  5. MetaProm: a neural network based meta-predictor for alternative human promoter prediction

    Directory of Open Access Journals (Sweden)

    Wang Junwen

    2007-10-01

    Full Text Available Abstract Background De novo eukaryotic promoter prediction is important for discovering novel genes and understanding gene regulation. In spite of the great advances made in the past decade, recent studies revealed that the overall performances of the current promoter prediction programs (PPPs are still poor, and predictions made by individual PPPs do not overlap each other. Furthermore, most PPPs are trained and tested on the most-upstream promoters; their performances on alternative promoters have not been assessed. Results In this paper, we evaluate the performances of current major promoter prediction programs (i.e., PSPA, FirstEF, McPromoter, DragonGSF, DragonPF, and FProm using 42,536 distinct human gene promoters on a genome-wide scale, and with emphasis on alternative promoters. We describe an artificial neural network (ANN based meta-predictor program that integrates predictions from the current PPPs and the predicted promoters' relation to CpG islands. Our specific analysis of recently discovered alternative promoters reveals that although only 41% of the 3' most promoters overlap a CpG island, 74% of 5' most promoters overlap a CpG island. Conclusion Our assessment of six PPPs on 1.06 × 109 bps of human genome sequence reveals the specific strengths and weaknesses of individual PPPs. Our meta-predictor outperforms any individual PPP in sensitivity and specificity. Furthermore, we discovered that the 5' alternative promoters are more likely to be associated with a CpG island.

  6. Quantitative Structure Pharmacokinetic Relationship Using Artificial Neural Network: A Review

    Directory of Open Access Journals (Sweden)

    S. K. Singh

    2009-10-01

    Full Text Available Quantitative structure activity relationship (QSAR has become a tool for designing in various areas like drugs, food additive, Pesticides, biochemical reactant, environmental pollutant and toxic products. In QSAR biological activity can be related with physicochemical properties and in QSPkR (Quantitative Structure Pharmacokinetic Relationship, pharmacokinetic properties can be related with physicochemical properties, relation found in terms of quantity. A number of literature and review article have been published on Quantitative structure pharmacokinetic relationship. But prediction of human pharmacokinetic properties of known and unknown is much difficult job in pharmaceutical industry. Pharmacokinetic data of animal cannot be put straightforward. Artificial neural network (ANN is used to predict the pharmacokinetic properties. Artificial neural network has basic structure like biological brain and compose of neurons which are interconnected to each other. The present review not only compiles the literature of QSPkR using ANN, but gives detail about the physicochemical properties and artificial neural network.

  7. Study on optimization control method based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    FU Hua; SUN Shao-guang; XU Zhen-Iiang

    2005-01-01

    In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limitations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advantages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With optimization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.

  8. Quantitative recognition of flammable and toxic gases with artificial neural network using metal oxide gas sensors in embedded platform

    Directory of Open Access Journals (Sweden)

    B. Mondal

    2015-06-01

    Full Text Available Artificial Neural Network (ANN based pattern recognition technique is used for ensuring the reliable evaluation of responses from an array of Zinc Oxide (ZnO based sensors comprising of pure ZnO nano-rods and composites of ZnO–SnO2. All the sensors were fabricated in the lab. The paper first reports the development of an artificial neural network based model for successfully recognizing different concentration of hydrogen, methane and carbon mono-oxide. Feed forward back propagation neural network was used for the classification of the gases at critical concentrations. The optimized ANN algorithm is then embedded in the microcontroller based circuit and finally verified under lab conditions.

  9. RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

    Science.gov (United States)

    Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour

    2012-09-01

    In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance.

  10. Artificial Neural Network in Harmonic Reduction of STATCOM

    Institute of Scientific and Technical Information of China (English)

    Li Hongmei; Li Zhenran; Zheng Peiying

    2005-01-01

    To eliminate harmonic pollution incurred from the static synchronous compensator(STATCOM), a method of applying artificial neural network is presented. When PWM wave is formed based on the harmonic suppression theory, a concave is set on certain angle of the square wave to suppress unnecessary harmonics, by timely and on-line determining the chopping angle corresponding to respective harmonics through artificial neural network, i.e. by setting the position of concave to eliminate corresponding harmonics, the harmonic component on output voltage of the inverter can be improved. To conclude through computer simulation test, the perfect control effect has been proved.

  11. AUTOMATED DEFECT CLASSIFICATION USING AN ARTIFICIAL NEURAL NETWORK

    International Nuclear Information System (INIS)

    The automated defect classification algorithm based on artificial neural network with multilayer backpropagation structure was utilized. The selected features of flaws were used as input data. In order to train the neural network it is necessary to prepare learning data which is representative database of defects. Database preparation requires the following steps: image acquisition and pre-processing, image enhancement, defect detection and feature extraction. The real digital radiographs of welded parts of a ship were used for this purpose.

  12. Neurons vs Weights Pruning in Artificial Neural Networks

    OpenAIRE

    Bondarenko, Andrey; Borisov, Arkady; Alekseeva, Ludmila

    2015-01-01

    Artificial neural networks (ANN) are well known for their good classification abilities. Recent advances in deep learning imposed second ANN renaissance. But neural networks possesses some problems like choosing hyper parameters such as neuron layers count and sizes which can greatly influence classification rate. Thus pruning techniques were developed that can reduce network sizes, increase its generalization abilities and overcome overfitting. Pruning approaches, in contrast to growing neur...

  13. PREDICTION OF LEAF SPRING PARAMETERS USING ARTIFICIAL NEURAL NETWORKS

    OpenAIRE

    Dr.D.V.V.KRISHNA PRASAD; J.P.KARTHIK

    2013-01-01

    In this paper an attempt is made to predict the optimum design parameters using artificial neural networks. For this static and dynamic analysis on various leaf spring configuration is carried out by ANSYS and is used as training data for neural network. Training data includes cross section of the leaf, load on the leaf spring, stresses, displacement and natural frequencies. By creating a network using thickness and width of the leaf, load on the leaf spring as input parameters and stresses, ...

  14. Application of Artificial Neural Network in Indicator Diagram

    Institute of Scientific and Technical Information of China (English)

    WuXiaodong; JiangHua; HanGuoqing

    2004-01-01

    Indicator diagram plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in shape identification. This paper illuminates ANN realization in identifying fault kinds of indicator diagrams, including a back-propagation algorithm, characteristics of the indicator diagram and some examples. It is concluded that the buildup of a neural network and the abstract of indicator diagrams are important to successful application.

  15. Research on Artificial Neural Network Method for Credit Application

    Institute of Scientific and Technical Information of China (English)

    MingxingLi; PingHeng; PeiwuDong

    2004-01-01

    Considering our country's present situation, in this paper we provide ten evaluation indexes of the credit application management, which is used as the input vector of neural network. Then we set up a three-layer back propagation model for the credit application evaluation based on the artificial neural network. We also analyzed the model using the real data; the testing result indicates that the model is a good method and a good tool.

  16. Artificial Neural Network Model for Optical Fiber Direction Coupler Design

    Institute of Scientific and Technical Information of China (English)

    李九生; 鲍振武

    2004-01-01

    A new approach to the design of the optical fiber direction coupler by using neural network is proposed. To train the artificial neural network,the coupling length is defined as the input sample, and the coupling ratio is defined as the output sample. Compared with the numerical value calculation of the theoretical formula, the error of the neural network model output is 1% less.Then, through the model, to design a broadband or a single wavelength optical fiber direction coupler becomes easy. The method is proved to be reliable, accurate and time-saving. So it is promising in the field of both investigation and application.

  17. Improved Local Weather Forecasts Using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Jørgensen, Bo Nørregaard

    2015-01-01

    using an artificial neural network. The neural network used is a NARX network, which is known to model non-linear systems well. The predictions are compared to both a design reference year as well as commercial weather forecasts based upon numerical modelling. The results presented in this paper show...... that the network outperforms the commercial forecast for lower step aheads (network’s performance is in the range of the commercial forecast. However, the neural network approach is fast, fairly precise and allows for further expansion with higher resolution....

  18. Data systems and computer science: Neural networks base R/T program overview

    Science.gov (United States)

    Gulati, Sandeep

    1991-01-01

    The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.

  19. Optimal control learning with artificial neural networks

    International Nuclear Information System (INIS)

    This paper shows neural networks capabilities in optimal control applications of non linear dynamic systems. Our method is issued of a classical method concerning the direct research of the optimal control using gradient techniques. We show that neural approach and backpropagation paradigm are able to solve efficiently equations relative to necessary conditions for an optimizing solution. We have taken into account the known capabilities of multi layered networks in approximation functions. And for dynamic systems, we have generalized the indirect learning of inverse model adaptive architecture that is capable to define an optimal control in relation to a temporal criterion. (orig.)

  20. Prediction of friction factor of pure water flowing inside vertical smooth and microfin tubes by using artificial neural networks

    Science.gov (United States)

    Çebi, A.; Akdoğan, E.; Celen, A.; Dalkilic, A. S.

    2016-06-01

    An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.

  1. Some structural determinants of Pavlovian conditioning in artificial neural networks

    NARCIS (Netherlands)

    Sanchez, Jose M.; Galeazzi, Juan M.; Burgos, Jose E.

    2010-01-01

    This paper investigates the possible role of neuroanatomical features in Pavlovian conditioning, via computer simulations with layered, feedforward artificial neural networks. The networks' structure and functioning are described by a strongly bottom-up model that takes into account the roles of hip

  2. Recurrent Artificial Neural Networks and Finite State Natural Language Processing.

    Science.gov (United States)

    Moisl, Hermann

    It is argued that pessimistic assessments of the adequacy of artificial neural networks (ANNs) for natural language processing (NLP) on the grounds that they have a finite state architecture are unjustified, and that their adequacy in this regard is an empirical issue. First, arguments that counter standard objections to finite state NLP on the…

  3. A Neuron- and a Synapse Chip for Artificial Neural Networks

    DEFF Research Database (Denmark)

    Lansner, John; Lehmann, Torsten

    1992-01-01

    A cascadable, analog, CMOS chip set has been developed for hardware implementations of artificial neural networks (ANN's):I) a neuron chip containing an array of neurons with hyperbolic tangent activation functions and adjustable gains, and II) a synapse chip (or a matrix-vector multiplier) where...

  4. Artificial neural networks as a tool in urban storm drainage

    DEFF Research Database (Denmark)

    Loke, E.; Warnaars, E.A.; Jacobsen, P.

    1997-01-01

    The introduction of Artificial Neural Networks (ANNs) as a tool in the field of urban storm drainage is discussed. Besides some basic theory on the mechanics of ANNs and a general classification of the different types of ANNs, two ANN application examples are presented: The prediction of runoff...

  5. Introducing Artificial Neural Networks through a Spreadsheet Model

    Science.gov (United States)

    Rienzo, Thomas F.; Athappilly, Kuriakose K.

    2012-01-01

    Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

  6. ChloroP, a neural network-based method for predicting chloroplast transitpeptides and their cleavage sites

    DEFF Research Database (Denmark)

    Emanuelsson, O.; Nielsen, Henrik; von Heijne, Gunnar

    1999-01-01

    We present a neural network based method (ChloroP) for identifying chloroplast transit peptides and their cleavage sites. Using cross-validation, 88% of the sequences in our homology reduced training set were correctly classified as transit peptides or nontransit peptides. This performance level...... is well above that of the publicly available chloroplast localization predictor PSORT. Cleavage sites are predicted using a scoring matrix derived by an automatic motif-finding algorithm. Approximately 60% of the known cleavage sites in our sequence collection were predicted to within +/-2 residues from...

  7. Mandarin Chinese Tone Recognition with an Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    XU Li; ZHANG Wenle; ZHOU Ning; LEE Chaoyang; LI Yongxin; CHEN Xiuwu; ZHAO Xiaoyan

    2006-01-01

    Mandarin Chinese tone patterns vary in one of the four ways, i.e, (1) high level; (2) rising; (3) low falling and rising; and (4) high falling. The present study is to examine the efficacy of an artificial neural network in recognizing these tone patterns. Speech data were recorded from 12 children (3-6 years of age) and 15 adults. All subjects were native Mandarin Chinese speakers. The fundamental frequencies (FO) of each monosyllabic word of the speech data were extracted with an autocorrelation method. The pitch data(i.e., the FO contours) were the inputs to a feed-forward backpropagation artificial neural network. The number of inputs to the neural network varied from 1 to 16 and the hidden layer of the network contained neurons that varied from 1 to 16 in number. The output of the network consisted of four neurons representing the four tone patterns of Mandarin Chinese. After being trained with the Levenberg-Marquardt optimization, the neural network was able to successfully classify the tone patterns with an accuracy of about 90% correct for speech samples from both adults and children. The artificial neural network may provide an objective and effective way of assessing tone production in prelingually-deafened children who have received cochlear implants.

  8. Evolving Spiking Neural Networks for Control of Artificial Creatures

    Directory of Open Access Journals (Sweden)

    Arash Ahmadi

    2013-10-01

    Full Text Available To understand and analysis behavior of complicated and intelligent organisms, scientists apply bio-inspired concepts including evolution and learning to mathematical models and analyses. Researchers utilize these perceptions in different applications, searching for improved methods andapproaches for modern computational systems. This paper presents a genetic algorithm based evolution framework in which Spiking Neural Network (SNN of artificial creatures are evolved for higher chance of survival in a virtual environment. The artificial creatures are composed ofrandomly connected Izhikevich spiking reservoir neural networks using population activity rate coding. Inspired by biological neurons, the neuronal connections are considered with different axonal conduction delays. Simulations results prove that the evolutionary algorithm has thecapability to find or synthesis artificial creatures which can survive in the environment successfully.

  9. A RECURRENT ELMAN NEURAL NETWORK - BASED APPROACH TO DETECT THE PRESENCE OF EPILEPTIC ATTACK IN ELECTROENCEPHALOGRAM (EEG SIGNALS

    Directory of Open Access Journals (Sweden)

    Mr.S.Sundaram

    2014-10-01

    Full Text Available Epileptic attack persons are detected largely on the analysis of Electroencephalogram (EEG signals. The EEG signals recordings generate very bulk data which require a skilled and careful analysis. This method can be automated based on Elman Neural Network by using a time frequency domain characteristics of EEG signal called Approximate Entropy (ApEn. This method consists of EEG collection of data, extraction and classification. EEG data from normal persons and epileptic affected persons was collected, digitized and then fed into the Elman neural network. This proposed system proposes a neural-network-based automated epileptic EEG detection system that uses approximate entropy (ApEn as the input feature. Approximate Entropy (ApEn [1] is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that the value of the Approximate Entropy drops sharply during an epileptic attack[2]and this fact is used in the proposed system. Type of a neural network namely, Elman neural network is considered in this paper. The experimental results portray that this proposed approach efficiently detects the presence of epileptic seizures[3] in EEG signals and showed a reasonable accuracy.

  10. Improved simultaneous estimation of tracer kinetic models with artificial immune network based optimization method.

    Science.gov (United States)

    Liu, L; Ding, H; Huang, H B

    2016-01-01

    Tracer kinetic modeling (TKM) is a promising quantitative method for physiological and biochemical processes in vivo. In this paper, we investigated the applications of an immune-inspired method to better address the issues of Simultaneous Estimation (SIME) of TKM with multimodal optimization. Experiments of dynamic FDG PET imaging experiments and simulation studies were carried out. The proposed artificial immune network (TKM_AIN) shows more scalable and effective when compared with the gradient-based Marquardt-Levenberg algorithm and the scholastic-based simulated annealing method. PMID:26433131

  11. Artificial neural networks in the nuclear engineering (Part 2)

    International Nuclear Information System (INIS)

    The field of Artificial Neural Networks (ANN), one of the branches of Artificial Intelligence has been waking up a lot of interest in the Nuclear Engineering (NE). ANN can be used to solve problems of difficult modeling, when the data are fail or incomplete and in high complexity problems of control. The first part of this work began a discussion with feed-forward neural networks in back-propagation. In this part of the work, the Multi-synaptic neural networks is applied to control problems. Also, the self-organized maps is presented in a typical pattern classification problem: transients classification. The main purpose of the work is to show that ANN can be successfully used in NE if a carefully choice of its type is done: the application sets this choice. (author)

  12. Techniques of Image Processing Based on Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    LI Wei-qing; WANG Qun; WANG Cheng-biao

    2006-01-01

    This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two artificial neural networks were made and the two problems were solved. The one solved chromatism classification. Hue,saturation and their probability of three colors, whose appearing probabilities were maximum in color histogram,were selected as input parameters, and the number of output node could be adjusted with the change of requirement. The other solved edge detection. In this neutral network, edge detection of gray scale image was able to be tested with trained neural networks for a binary image. It prevent the difficulty that the number of needed training samples was too large if gray scale images were directly regarded as training samples. This system is able to be applied to not only glass steel fault inspection but also other product online quality inspection and classification.

  13. Transient stability Assessment using Artificial Neural Network Considering Fault Location

    Directory of Open Access Journals (Sweden)

    P.K.Olulope

    2010-06-01

    Full Text Available This paper describes the capability of artificial neural network for predicting the critical clearing time of power system. It combines the advantages of time domain integration schemes with artificial neural network for real time transient stability assessment. The training of ANN is done using selected features as input and critical fault clearing time (CCT as desire target. A single contingency was applied and the target CCT was found using time domain simulation. Multi layer feed forward neural network trained with Levenberg Marquardt (LM back propagation algorithm is used to provide the estimated CCT. The effectiveness of ANN, the method is demonstrated on single machine infinite bus system (SMIB. The simulation shows that ANN can provide fast and accurate mapping which makes it applicable to real time scenario.

  14. Probabilistic Wind Power Forecasting with Hybrid Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wan, Can; Song, Yonghua; Xu, Zhao;

    2016-01-01

    probabilities of prediction errors provide an alternative yet effective solution. This article proposes a hybrid artificial neural network approach to generate prediction intervals of wind power. An extreme learning machine is applied to conduct point prediction of wind power and estimate model uncertainties...... actual wind power data from Denmark. The numerical results demonstrate that the proposed hybrid artificial neural network approach is effective and efficient for probabilistic forecasting of wind power and has high potential in practical applications....... via a bootstrap technique. Subsequently, the maximum likelihood estimation method is employed to construct a distinct neural network to estimate the noise variance of forecasting results. The proposed approach has been tested on multi-step forecasting of high-resolution (10-min) wind power using...

  15. Application of artificial neural network based on the genetic algorithm in predicting the root distribution of winter wheat%基于遗传算法的人工神经网络模型在冬小麦根系分布预报中的应用

    Institute of Scientific and Technical Information of China (English)

    罗长寿; 左强; 李保国

    2004-01-01

    In this study, a controlled experiment of winter wheat under water stress at the seedling stage was conducted in soil columns in greenhouse. Based on the data gotten from the experiment, a model to estimate root length density distribution was developed through optimizing the weights of neural network by genetic algorithm. The neural network model was constructed by using forward neural network framework, by applying the strategy of the roulette wheel selection and reserving the most optimizing series of weights, which were composed by real codes.This model was applied to predict the root length density distribution of winter wheat, and the predicted root length density had good agreement with experiment data. The way could save a lot of manpower and material resources for determining the root length density distribution of winter wheat.

  16. The study of fuzzy chaotic neural network based on chaotic method

    Institute of Scientific and Technical Information of China (English)

    WANG Ke-jun; TANG Mo; ZHANG Yan

    2006-01-01

    This paper proposes a type of Fuzzy Chaotic Neural Network (FCNN). Firstly, the model of recurrent fuzzy neural network (RFNN) is considered, which adds a feedback in the second layer to realize dynamic map. Then, the Logistic map is introduced into the recurrent fuzzy neural network, so as to build a Fuzzy Chaotic Neural Network (FCNN). Its chaotic character is analyzed, and then the training algorithm and associate memory ability are studied subsequently. And then, a chaotic system is approximated using FCNN; the simulation results indicate that FCNN could approach dynamic system preferably. And owing to the introducing of chaotic map, the chaotic recollect capacity of FCNN is increased.

  17. Use of Artificial Neural Networks for Prediction of Convective Heat Transfer in Evaporative Units

    Directory of Open Access Journals (Sweden)

    Romero-Méndez Ricardo

    2014-01-01

    Full Text Available Convective heat transfer prediction of evaporative processes is more complicated than the heat transfer prediction of single-phase convective processes. This is due to the fact that physical phenomena involved in evaporative processes are very complex and vary with the vapor quality that increases gradually as more fluid is evaporated. Power-law correlations used for prediction of evaporative convection have proved little accuracy when used in practical cases. In this investigation, neural-network-based models have been used as a tool for prediction of the thermal performance of evaporative units. For this purpose, experimental data were obtained in a facility that includes a counter-flow concentric pipes heat exchanger with R134a refrigerant flowing inside the circular section and temperature controlled warm water moving through the annular section. This work also included the construction of an inverse Rankine refrigeration cycle that was equipped with measurement devices, sensors and a data acquisition system to collect the experimental measurements under different operating conditions. Part of the data were used to train several neural-network configurations. The best neural-network model was then used for prediction purposes and the results obtained were compared with experimental data not used for training purposes. The results obtained in this investigation reveal the convenience of using artificial neural networks as accurate predictive tools for determining convective heat transfer rates of evaporative processes.

  18. A Neural Network Based Diagnostic System for Classification of Industrial Carrying Jobs With Respect of Low and High Musculoskeletal Injury Risk

    Directory of Open Access Journals (Sweden)

    Rohit Sharma

    2012-02-01

    Full Text Available Even with many years of research efforts, Safety professionals and ergonomists have not yetbeen established the occupational exposure limits of different risk factors for development ofMusculoskeletal disorders (MSDs. One of the main problems in setting such guidelines is toaccurately assess the association between exposures and possible occupational disorders ordiseases and predict the outcome of any variable. The task of an industrial ergonomist iscomplicated because the potential risk factors that may contribute to the onset of the MSDsinteract in a complex way, and require an analyst to apply elaborate data measurement andcollection techniques for a realistic job analysis. This makes it difficult to discriminate wellbetween the jobs that place workers at high or low risk of MSDs. This paper describes a newapproach for the development of artificial neural networks applied to classifying the risk of MSDsfor industrial carrying jobs. The data set used in this research was collected from Foundry andSugar industries workers using the physiological variables.The main objective of this study was toto develop an artificial neural network based diagnostic system which can classify industrial jobsaccording to the potential risk for physiological stressors due to workplace design. The neuralnetwork obtained can be used by the ergonomist as a diagnostic system, enabling jobs to beclassified into two categories (low-risk and high-risk according to the associated likelihood ofcausing MSDs. This system provides a higher proportion of correct classifications than otherprevious models. So, the system can be used as an expert system which, when properly trained,will classify carrying load by male and female industry workers into two categories of low risk andhigh risk work, based on the available characteristics factors.

  19. A NOVEL ARTIFICIAL HYDROCARBON NETWORKS BASED SPACE VECTOR PULSE WIDTH MODULATION CONTROLLER FOR INDUCTION MOTORS

    Directory of Open Access Journals (Sweden)

    Hiram Ponce

    2014-01-01

    Full Text Available Most of machine-operated industrial processes implement electric machinery as their work sources, implying the necessary improvement of control techniques and power electronics drivers. Many years have passed since the control conflicts related to induction motors have been overcome through torque-flux control techniques so their advantages over direct current motors have made them to be the most common electric actuator found behind industrial automation. In fact, induction motors can be easily operated using a Direct Torque Control (DTC. Since, it is based on a hysteresis control of the torque and flux errors, its performance is characterized by a quick reaching of the set point, but also a high ripple on both torque and flux. In order to enhance that technique, this study introduces a novel hybrid fuzzy controller with artificial hydrocarbon networks (FMC that is used in a Space Vector Pulse Width Modulation (SVPWM technique, so-called FMC-SVPWM-DTC. In fact, this study describes the proposal and its design method. Experimental results over a velocity-torque cascade topology proved that the proposed FMC-SVPWM-DTC responses highly effective almost suppressing rippling in torque and flux. It also performed a faster speed response than in a conventional DTC. In that sense, the proposed FMC-SVPWM-DTC can be used an alternative approach for controlling induction motors.

  20. Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm

    OpenAIRE

    Sudip Mandal; Abhinandan Khan; Goutam Saha; Pal, Rajat K.

    2016-01-01

    The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology h...

  1. Stability analysis of extended discrete-time BAM neural networks based on LMI approach

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory (BAM) neural networks. By using the Euler rule, we discretize the continuous-time BAM neural networks as the extended discrete-time BAM neural networks with non-threshold activation functions. Here we present some conditions under which the neural networks have unique equilibrium points. To judge the global asymptotic stability of the equilibrium points, we introduce a new neural network model - standard neural network model (SNNM).For the SNNMs, we derive the sufficient conditions for the global asymptotic stability of the equilibrium points, which are formulated as some linear matrix inequalities (LMIs). We transform the discrete-time BAM into the SNNM and apply the general result about the SNNM to the determination of global asymptotic stability of the discrete-time BAM. The approach proposed extends the known stability results, has lower conservativeness, can be verified easily, and can also be applied to other forms of recurrent neural networks.

  2. Neural Network Based on Rough Sets and Its Application to Remote Sensing Image Classification

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    This paper presents a new kind of back propagation neural network (BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the survey and analysis of RBPNN for the classification of remote sensing multi-spectral image is discussed.The successful application of RBPNN to a land cover classification illustrates the simple computation and high accuracy of the new neural network and the flexibility and practicality of this new approach.

  3. Camera characterization using back-propagation artificial neutral network based on Munsell system

    Science.gov (United States)

    Liu, Ye; Yu, Hongfei; Shi, Junsheng

    2008-02-01

    The camera output RGB signals do not directly corresponded to the tristimulus values based on the CIE standard colorimetric observer, i.e., it is a device-independent color space. For achieving accurate color information, we need to do color characterization, which can be used to derive a transformation between camera RGB values and CIE XYZ values. In this paper we set up a Back-Propagation (BP) artificial neutral network to realize the mapping from camera RGB to CIE XYZ. We used the Munsell Book of Color with total number 1267 as color samples. Each patch of the Munsell Book of Color was recorded by camera, and the RGB values could be obtained. The Munsell Book of Color were taken in a light booth and the surround was kept dark. The viewing/illuminating geometry was 0/45 using D 65 illuminate. The lighting illuminating the reference target needs to be as uniform as possible. The BP network was a 5-layer one and (3-10-10-10-3), which was selected through our experiments. 1000 training samples were selected randomly from the 1267 samples, and the rest 267 samples were as the testing samples. Experimental results show that the mean color difference between the reproduced colors and target colors is 0.5 CIELAB color-difference unit, which was smaller than the biggest acceptable color difference 2 CIELAB color-difference unit. The results satisfy some applications for the more accurate color measurements, such as medical diagnostics, cosmetics production, the color reappearance of different media, etc.

  4. Static human face recognition using artificial neural networks

    International Nuclear Information System (INIS)

    This paper presents a novel method of human face recognition using digital computers. A digital PC camera is used to take the BMP images of the human faces. An artificial neural network using Back Propagation Algorithm is developed as a recognition engine. The BMP images of the faces serve as the input patterns for this engine. A software 'Face Recognition' has been developed to recognize the human faces for which it is trained. Once the neural network is trained for patterns of the faces, the software is able to detect and recognize them with success rate of about 97%. (author)

  5. Artificial Neural Networks for Thermochemical Conversion of Biomass

    DEFF Research Database (Denmark)

    Puig Arnavat, Maria; Bruno, Joan Carles

    2015-01-01

    Artificial neural networks (ANNs), extensively used in different fields, have been applied for modeling biomass gasification processes in fluidized bed reactors. Two ANN models are presented, one for circulating fluidized bed gasifiers and another for bubbling fluidized bed gasifiers. Both models...... other authors. The obtained results show that the percentage composition of the main four gas species in producer gas (CO, CO2, H2, CH4) and producer gas yield for a biomass fluidized bed gasifier, can be successfully predicted by applying neural networks. The results obtained show high agreement...

  6. A Novel Evolutionary Feedforward Neural Network with Artificial Immunology

    Institute of Scientific and Technical Information of China (English)

    宫新保; 臧小刚; 周希朗

    2003-01-01

    A hybrid algorithm to design the multi-layer feedforward neural network was proposed. Evolutionaryprogramming is used to design the network that makes the training process tending to global optima. Artificial im-munology combined with simulated annealing algorithm is used to specify the initial weight vectors, therefore improves the probabiligy of training algorithm to converge to global optima. The applications of the neural networkin the modulation-style recognition of analog modulated rader signals demonstrate the good performance of the net-work.

  7. Modelling of word usage frequency dynamics using artificial neural network

    International Nuclear Information System (INIS)

    In this paper the method for modelling of word usage frequency time series is proposed. An artificial feedforward neural network was used to predict word usage frequencies. The neural network was trained using the maximum likelihood criterion. The Google Books Ngram corpus was used for the analysis. This database provides a large amount of data on frequency of specific word forms for 7 languages. Statistical modelling of word usage frequency time series allows finding optimal fitting and filtering algorithm for subsequent lexicographic analysis and verification of frequency trend models

  8. Speed up Training of the Recurrent Neural Network Based on Constrained Optimization Techniques

    Institute of Scientific and Technical Information of China (English)

    陈珂; 包威权; 等

    1996-01-01

    In this paper,the constrained optimization technique for a substantial problem is explored,that is accelerating training the globally recurrent neural network.Unlike most of the previous methods in feedforware neural networks,the authors adopt the constrained optimization technique to improve the gradientbased algorithm of the globally recurrent neural network for the adaptive learning rate during tracining.Using the recurrent network with the improved algorithm,some experiments in two real-world problems,namely,filtering additive noises in acoustic data and classification of temporat signals for speaker identification,have been performed.The experimental results show that the recurrent neural network with the improved learning algorithm yields significantly faster training and achieves the satisfactory performance.

  9. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

    Science.gov (United States)

    Manikandan, Narayanan; Subha, Srinivasan

    2016-01-01

    Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

  10. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Narayanan Manikandan

    2016-01-01

    Full Text Available Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

  11. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

    Science.gov (United States)

    Manikandan, Narayanan; Subha, Srinivasan

    2016-01-01

    Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used. PMID:26881271

  12. An Output-Recurrent-Neural-Network-Based Iterative Learning Control for Unknown Nonlinear Dynamic Plants

    OpenAIRE

    Ying-Chung Wang; Chiang-Ju Chien

    2012-01-01

    We present a design method for iterative learning control system by using an output recurrent neural network (ORNN). Two ORNNs are employed to design the learning control structure. The first ORNN, which is called the output recurrent neural controller (ORNC), is used as an iterative learning controller to achieve the learning control objective. To guarantee the convergence of learning error, some information of plant sensitivity is required to design a suitable adaptive law for the ORNC. Hen...

  13. Neural network based approach for tuning of SNS feedback and feedforward controllers

    International Nuclear Information System (INIS)

    The primary controllers in the SNS low level RF system are proportional-integral (PI) feedback controllers. To obtain the best performance of the linac control systems, approximately 91 individual PI controller gains should be optimally tuned. Tuning is time consuming and requires automation. In this paper, a neural network is used for the controller gain tuning. A neural network can approximate any continuous mapping through learning. In a sense, the cavity loop PI controller is a continuous mapping of the tracking error and its one-sample-delay inputs to the controller output. Also, monotonic cavity output with respect to its input makes knowing the detailed parameters of the cavity unnecessary. Hence the PI controller is a prime candidate for approximation through a neural network. Using mean square error minimization to train the neural network along with a continuous mapping of appropriate weights, optimally tuned PI controller gains can be determined. The same neural network approximation property is also applied to enhance the adaptive feedforward controller performance. This is done by adjusting the feedforward controller gains, forgetting factor, and learning ratio. Lastly, the automation of the tuning procedure data measurement, neural network training, tuning and loading the controller gain to the DSP is addressed.

  14. Forecast Share Prices with Artificial Neural Network in Crisis Periods

    Directory of Open Access Journals (Sweden)

    Feyyaz Zeren

    2014-09-01

    Full Text Available Crisis periods present quite a significant moment for financial markets. Considering not losing and changing the crisis periods into opportunities, forecasts of share prices during these periods have an importance for the investors. In this study, daily closing prices of Borsa Istanbul National 100 index during the three big crisis periods, as 1994, 2001, and 2008, have been tried to be forecasted, by using artificial neural networks. As a result of this study, it is determined that in the forecasts of Borsa Istanbul, artificial neural networks show high performance. This result was proved by both comparing the values that occurred and forecasted on the graphics, and Mean Absolute Percentage Error (MAPE calculations

  15. Numerical solution of differential equations by artificial neural networks

    Science.gov (United States)

    Meade, Andrew J., Jr.

    1995-01-01

    Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks (ANN's) are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed by the author to mate the adaptability of the ANN with the speed and precision of the digital computer. This method has been successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

  16. Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Mehdi Nikoo

    2015-01-01

    Full Text Available Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs as a combination of artificial neural network (ANN and evolutionary search procedures, such as genetic algorithms (GA. In this paper for purpose of constructing models samples of cylindrical concrete parts with different characteristics have been used with 173 experimental data patterns. Water-cement ratio, maximum sand size, amount of gravel, cement, 3/4 sand, 3/8 sand, and coefficient of soft sand parameters were considered as inputs; and using the ANN models, the compressive strength of concrete is calculated. Moreover, using GA, the number of layers and nodes and weights are optimized in ANN models. In order to evaluate the accuracy of the model, the optimized ANN model is compared with the multiple linear regression (MLR model. The results of simulation verify that the recommended ANN model enjoys more flexibility, capability, and accuracy in predicting the compressive strength of concrete.

  17. Artificial neural networks technology for neutron spectrometry and dosimetry

    International Nuclear Information System (INIS)

    Artificial Neural Network Technology has been applied to unfold neutron spectra and to calculate 13 dosimetric quantities using seven count rates from a Bonner Sphere Spectrometer with a 6LiI(Eu). Two different networks, one for spectrometry and another for dosimetry, were designed. To train and test both networks, 177 neutron spectra from the IAEA compilation were utilised. Spectra were re-binned into 31 energy groups, and the dosimetric quantities were calculated using the MCNP code and the fluence-to-dose conversion coefficients from ICRP 74. Neutron spectra and UTA4 response matrix were used to calculate the expected count rates in the Bonner spectrometer. Spectra and H*(10) of 239PuBe and 241AmBe were experimentally obtained and compared with those determined with the artificial neural networks. (authors)

  18. Application of artificial neural networks to micro gas turbines

    Energy Technology Data Exchange (ETDEWEB)

    Bartolini, C.M.; Caresana, F.; Comodi, G.; Pelagalli, L.; Renzi, M.; Vagni, S. [Dipartimento di Energetica, Facolta di Ingegneria, Universita Politecnica delle Marche, via Brecce Bianche, 60131 Ancona (Italy)

    2011-01-15

    In this work, artificial neural networks (ANNs) were applied to describe the performance of a micro gas turbine (MGT). In particular, they were used (i) to complete performance diagrams for unavailable experimental data; (ii) to assess the influence of ambient parameters on performance; and (iii) to analyze and predict emissions of pollutants in the exhausts. The experimental data used to feed the ANNs were acquired from a manufacturer's test bed. Though large, the data set did not cover the whole working range of the turbine; ANNs and an artificial neural fuzzy interference system (ANFIS) were therefore applied to fill information gaps. The results of this investigation were also used for sensitivity analysis of the machine's behavior in different ambient conditions. ANNs can effectively evaluate both MGT performance and emissions in real installations in any climate, the worst R{sup 2} in the validation set being 0.9962. (author)

  19. Applications of artificial neural network chips

    International Nuclear Information System (INIS)

    In a collaboration between CERN and Royal Institute of Technology Stockholm a so called Asynchronous Transfer Mode (ATM) test setup was developed. The main goal of the task was the experimental verification of the harware design principles and methods, partly the application of the test setup for testing the neural network controlled self-routing, asynchronous event-building ATM networks. We took part in the first implementation of the IBM Zero Instruction Set Computer (ZISC036)[2] on a PC-486 ISA-bus card. This chip has been designed for cost-effective recognition and classification in real time. After building the PC interface card and testing the main functions of the built-in logic a code for character recognition was developed for comparing its performance to other RBF-type methods. The results show that the ZISC036 is performing quite well. The most attractive feature of the chip is the speed: if it is operated at 20 MHz, 64 component the evaluation is ready in 0.5 μ sec. (K.A.) 2 refs.; 1 fig

  20. Using Artificial Neural Networks To Forecast Financial Time Series

    OpenAIRE

    Aamodt, Rune

    2010-01-01

    This thesis investigates the application of artificial neural networks (ANNs) for forecasting financial time series (e.g. stock prices).The theory of technical analysis dictates that there are repeating patterns that occur in the historic prices of stocks, and that identifying these patterns can be of help in forecasting future price developments. A system was therefore developed which contains several ``agents'', each producing recommendations on the stock price based on some aspect of techn...

  1. Application of artificial neural networks in particle physics

    International Nuclear Information System (INIS)

    The application of artificial neural networks in particle physics is reviewed. The use of feed-forward nets is most common for event classification and function approximation. This network type is best suited for a hardware implementation and special VLSI chips are available which are used in fast trigger processors. Also discussed are fully connected networks of the Hopfield type for pattern recognition in tracking detectors. (orig.)

  2. Artificial neural networks : applications in morphometric and landscape features analysis

    OpenAIRE

    Ehsani, Amir Houshang

    2007-01-01

    In this thesis a semi-automatic method is developed to analyze morphometric features and landscape elements based on Self Organizing Map (SOM) as a unsupervised Artificial Neural Network algorithm. Analysis and parameterization of topography into simple and homogenous land elements (landform) can play an important role as basic information in planning processes and environmental modeling. Landforms and land cover are the main components of landscapes. Landscapes are dynamic systems that invol...

  3. Time series prediction using artificial neural network for power stabilization

    International Nuclear Information System (INIS)

    Time series prediction has been applied to many business and scientific applications. Prominent among them are stock market prediction, weather forecasting, etc. Here, this technique has been applied to forecast plasma torch voltages to stabilize power using a backpropagation, a model of artificial neural network. The Extended-Delta-Bar-Delta algorithm is used to improve the convergence rate of the network and also to avoid local minima. Results from off-line data was quite promising to use in on-line

  4. Application of artificial neural networks in particle physics

    International Nuclear Information System (INIS)

    The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the use of feed-forward nets for event classification and function approximation. This network type is best suited for a hardware implementation and special VLSI chips are available which are used in fast trigger processors. Also discussed are fully connected networks of the Hopfield type for pattern recognition in tracking detectors. (orig.)

  5. Artificial Neural Networks in Applications of Industrial Robots

    Institute of Scientific and Technical Information of China (English)

    王克胜; JonathanLienhardt; 袁庆丰; 方明伦

    2004-01-01

    Artificial neural networks (ANNs) have been widely used to solve a number of problems to which analytical solutions are difficult to obtain using traditional mathematical approaches.Such problems exist also in the analysis of industrial robots. This paper presents an overview of ANN applications to robot kinematics, dynamics,control, trajectory and path planning, and sensing. Reasons for using or not using ANNs to industrial robots are explained as well.

  6. Evolving Spiking Neural Networks for Control of Artificial Creatures

    OpenAIRE

    Arash Ahmadi

    2013-01-01

    To understand and analysis behavior of complicated and intelligent organisms, scientists apply bio-inspired concepts including evolution and learning to mathematical models and analyses. Researchers utilize these perceptions in different applications, searching for improved methods andapproaches for modern computational systems. This paper presents a genetic algorithm based evolution framework in which Spiking Neural Network (SNN) of artificial creatures are evolved for higher chance of survi...

  7. Activated sludge process based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    张文艺; 蔡建安

    2002-01-01

    Considering the difficulty of creating water quality model for activated sludge system, a typical BP artificial neural network model has been established to simulate the operation of a waste water treatment facilities. The comparison of prediction results with the on-spot measurements shows the model, the model is accurate and this model can also be used to realize intelligentized on-line control of the wastewater processing process.

  8. Using particle swarm optimization algorithm in an artificial neural network to forecast the strength of paste filling material

    Institute of Scientific and Technical Information of China (English)

    CHANG Qing-liang; ZHOU Hua-qiang; HOU Chao-jiong

    2008-01-01

    In order to forecast the strength of filling material exactly, the main factors affecting the strength of filling material are analyzed. The model of predicting the strength of filling material was established by applying the theory of artificial neural networks. Based on cases related to our test data of filling material, the predicted results of the model and measured values are compared and analyzed. The results show that the model is feasible and scientifically justified to predict the strength of filling material,which provides a new method for forecasting the strength of filling material for paste filling in coal mines.

  9. RECOGNITION OF CDNA MICROARRAY IMAGE USING FEEDFORWARD ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    R. M. Farouk

    2014-09-01

    Full Text Available The complementary DNA (cDNA sequence considered the magic biometric technique for personal identification. Microarray image processing used for the concurrent genes identification. In this paper, we present a new method for cDNA recognition based on the artificial neural network (ANN. We have segmented the location of the spots in a cDNA microarray. Thus, a precise localization and segmenting of a spot are essential to obtain a more exact intensity measurement, leading to a more accurate gene expression measurement. The segmented cDNA microarray image resized and used as an input for the proposed artificial neural network. For matching and recognition, we have trained the artificial neural network. Recognition results are given for the galleries of cDNA sequences . The numerical results show that, the proposed matching technique is an effective in the cDNA sequences process. The experimental results of our matching approach using different databases shows that, the proposed technique is an effective matching performance.

  10. The importance of artificial neural networks in biomedicine

    Energy Technology Data Exchange (ETDEWEB)

    Burke, H.B. [New York Medical College, Valhalla, NY (United States)

    1995-12-31

    The future explanatory power in biomedicine will be at the molecular-genetic level of analysis (rather than the epidemiologic-demographic or anatomic-cellular levels). This is the level of complex systems. Complex systems are characterized by nonlinearity and complex interactions. It is difficult for traditional statistical methods to capture complex systems because traditional methods attempt to find the model that best fits the statistician`s understanding of the phenomenon; complex systems are difficult to understand and therefore difficult to fit with a simple model. Artificial neural networks are nonparametric regression models. They can capture any phenomena, to any degree of accuracy (depending on the adequacy of the data and the power of the predictors), without prior knowledge of the phenomena. Further, artificial neural networks can be represented, not only as formulae, but also as graphical models. Graphical models can increase analytic power and flexibility. Artificial neural networks are a powerful method for capturing complex phenomena, but their use requires a paradigm shift, from exploratory analysis of the data to exploratory analysis of the model.

  11. Study on the fitting ways of artificial neural networks

    Institute of Scientific and Technical Information of China (English)

    SHAO Liang-shan; WANG Jun; SUN Shao-guang

    2008-01-01

    Function simulation, which is called virtual reality too, is popularly applied to solve uncertain problems. Good performance of hidden layers and perfect capability of function simulation make artificial neural networks one of the best choices to simulate functions with form unknown. Inputs and outputs were used to train the structure of the artificial neural network to make the outputs of network vary with the given inputs and keep consistent with the original data within tolerance. However, we couldn't get expected results by using samples of a simple two-variable-model for the cause of dimensional difference. The way of artificial neural networks to fit functions, which uses "multi-dimensional surface" of high dimension to fit "multi-dimensional line" of low dimension, was proved; the conclusion that good effects of fitting don't mean good function modeling when a dimensional difference exists was provided, and a suggestion of "surface collecting" in practical engineering application was proposed when collecting useful data.

  12. Two-dimensional magnetic modeling of ferromagnetic materials by using a neural networks based hybrid approach

    Science.gov (United States)

    Cardelli, E.; Faba, A.; Laudani, A.; Lozito, G. M.; Riganti Fulginei, F.; Salvini, A.

    2016-04-01

    This paper presents a hybrid neural network approach to model magnetic hysteresis at macro-magnetic scale. That approach aims to be coupled together with numerical treatments of magnetic hysteresis such as FEM numerical solvers of the Maxwell's equations in time domain, as in case of the non-linear dynamic analysis of electrical machines, and other similar devices, allowing a complete computer simulation with acceptable run times. The proposed Hybrid Neural System consists of four inputs representing the magnetic induction and magnetic field components at each time step and it is trained by 2D and scalar measurements performed on the magnetic material to be modeled. The magnetic induction B is assumed as entry point and the output of the Hybrid Neural System returns the predicted value of the field H at the same time step. Within the Hybrid Neural System, a suitably trained neural network is used for predicting the hysteretic behavior of the material to be modeled. Validations with experimental tests and simulations for symmetric, non-symmetric and minor loops are presented.

  13. Artificial neural network modeling of dissolved oxygen in reservoir.

    Science.gov (United States)

    Chen, Wei-Bo; Liu, Wen-Cheng

    2014-02-01

    The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan. PMID:24078053

  14. Neural network-based computer-aided diagnosis in distinguishing malignant from benign solitary pulmonary nodules by computed tomography

    Institute of Scientific and Technical Information of China (English)

    CHEN Hui; WANG Xiao-hua; MA Da-qing; MA Bin-rong

    2007-01-01

    Background Computer-aided diagnosis (CAD) of lung cancer is the subject of many current researches. Statistical methods and artificial neural networks have been applied to more quantitatively characterize solitary pulmonary nodules (SPNs). In this study, we developed a CAD scheme based on an artificial neural network to distinguish malignant from benign SPNs on thin-section computed tomography (CT) images, and investigated how the CAD scheme can help radiologists with different levels of experience make diagnostic decisions.Methods Two hundred thin-section CT images of SPNs with proven diagnoses (135 small peripheral lung cancers and 65 benign nodules) were analyzed. Three clinical features and nine CT signs of each case were studied by radiologists,and the indices of qualitative diagnosis were quantified. One hundred and forty nodules were selected randomly to form training samples, on which the neural network model was built. The remaining 60 nodules, forming test samples, were presented to 9 radiologists with 3-20 years of clinical experience, accompanied by standard reference images. The radiologists were asked to determine whether a nodule was malignant or benign first without and then with CAD output.Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis.Results CAD outputs on test samples had higher agreement with pathological diagnoses (Kappa=0.841, P<0.001).Compared with diagnostic results without CAD output, the average area under the ROC curve with CAD output was 0.96(P<0.001) for junior radiologists, 0.94 (P=0.014) for secondary radiologists and 0.96 (P=0.221) for senior radiologists,respectively. The differences in diagnostic performance with CAD output among the three levels of radiologists were not statistically significant (P=0.584, 0.920 and 0.707, respectively).Conclusions This CAD scheme based on an artificial neural network could improve diagnostic performance and assist radiologists in distinguishing

  15. Development and Evolution of Neural Networks in an Artificial Chemistry

    CERN Document Server

    Astor, J C; Astor, Jens C.; Adami, Christoph

    1998-01-01

    We present a model of decentralized growth for Artificial Neural Networks (ANNs) inspired by the development and the physiology of real nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates modeled by a simple artificial chemistry. Gene expression is manifested as axon and dendrite growth, cell division and differentiation, substrate production and cell stimulation. We demonstrate the model's power with a hand-written genome that leads to the growth of a simple network which performs classical conditioning. To evolve more complex structures, we implemented a platform-independent, asynchronous, distributed Genetic Algorithm (GA) that allows users to participate in evolutionary experiments via the World Wide Web.

  16. Comparisons of Short Term Load Forecasting using Artificial Neural Network and Regression Method

    Directory of Open Access Journals (Sweden)

    Rajesh Deshmukh

    2011-12-01

    Full Text Available In power systems the next day’s power generation must be scheduled every day, day ahead short-term load forecasting (STLF is a necessary daily task for power dispatch. Its accuracy affects the economic operation and reliability of the system greatly. Under prediction of STLF leads to insufficient reserve capacity preparation and in turn, increases the operating cost by using expensive peaking units. On the other hand, over prediction of STLF leads to the unnecessarily large reserve capacity, which is also related to high operating cost. the research work in this area is still a challenge to the electrical engineering scholars because of its high complexity. How to estimate the future load with the historical data has remained a difficulty up to now, especially for the load forecasting of holidays, days with extreme weather and other anomalous days. With the recent development of new mathematical, data mining and artificial intelligence tools, it is potentially possible to improve the forecasting result. This paper presents a new neural network based approach for short-term load forecasting that uses the most correlated weather data for training, validating and testing the neural network. Correlation analysis of weather data determines the input parameters of the neural networks. And its results compare to regression method.

  17. Prediction of Skin Penetration using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Sangita Saini,

    2010-06-01

    Full Text Available The artificial neural networks (ANN technologies provide on-line capability to analyze many inputs and provide information to multiple outputs, and have the capability to learn or adapt to changing conditions. No doubt that the determination of Skin permeability is a time consuming process; which involves a quite tedious work. Material and method: Software Neurodimension was used for this study. A data set was taken from literature and used to train the network. A set of 20 compounds were used to construct the ANN models for training and 10 compounds used for prediction of skin penetration (n=30, molecular weight>500 da. Skin permeability expressed in log Kp (cm/h. Abraham descriptors of R2 (excess molar refraction, π2 H dipolarity/polarizability, Σα2 H, Σβ2 H (the overall or effective hydrogen-bond acidity and basicity, and Vx (the McGowan haracteristic volume were obtained. Result: The correlation between the skin permeability coefficient and the Abraham descriptors were obtained from the trained neural network. The regression coefficient was 0.856 for training subset and MSE was 0.04. In addition, thepredictability of the neural network model was compared to the experimental data. This paper uses artificial neural network for prediction of Skin permeability study.

  18. DESIGN AND ANALOG VLSI IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    D.Yammenavar

    2011-08-01

    Full Text Available Nature has evolved highly advanced systems capable of performing complex computations, adoption andlearning using analog computations. Furthermore nature has evolved techniques to deal with impreciseanalog computations by using redundancy and massive connectivity. In this paper we are making use ofArtificial Neural Network to demonstrate the way in which the biological system processes in analogdomain.We are using 180nm CMOS VLSI technology for implementing circuits which performs arithmeticoperations and for implementing Neural Network. The arithmetic circuits presented here are based onMOS transistors operating in subthreshold region. The basic blocks of artificial neuron are multiplier,adder and neuron activation function.The functionality of designed neural network is verified for analog operations like signal amplificationand frequency multiplication. The network designed can be adopted for digital operations like AND, ORand NOT. The network realizes its functionality for the trained targets which is verified using simulationresults. The schematic, Layout design and verification of proposed Neural Network is carried out usingCadence Virtuoso tool.

  19. Evolutionary Algorithm for Optimal Connection Weights in Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    G.V.R. Sagar, S. Venkata Chalam, Manoj Kumar Singh

    2011-12-01

    Full Text Available A neural network may be considered as an adaptive system that progressively self-organizes inorder to approximate the solution, making the problem solver free from the need to accuratelyand unambiguously specify the steps towards the solution. Moreover, Evolutionary computationcan be integrated with artificial Neural Network to increase the performance at various levels; inresult such neural network is called Evolutionary ANN. In this paper very important issue of neuralnetwork namely adjustment of connection weights for learning presented by Genetic algorithmover feed forward architecture. To see the performance of developed solution comparison hasgiven with respect to well established method of learning called gradient decent method. Abenchmark problem of classification, XOR, has taken to justify the experiment. Presented methodis not only having very probability to achieve the global minima but also having very fastconvergence.

  20. Optimizing Artificial Neural Networks using Cat Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    John Paul T. Yusiong

    2012-12-01

    Full Text Available An Artificial Neural Network (ANN is an abstract representation of the biological nervous system which has the ability to solve many complex problems. The interesting attributes it exhibits makes an ANN capable of “learning”. ANN learning is achieved by training the neural network using a training algorithm. Aside from choosing a training algorithm to train ANNs, the ANN structure can also be optimized by applying certain pruning techniques to reduce network complexity. The Cat Swarm Optimization (CSO algorithm, a swarm intelligence-based optimization algorithm mimics the behavior of cats, is used as the training algorithm and the Optimal Brain Damage (OBD method as the pruning algorithm. This study suggests an approach to ANN training through the simultaneous optimization of the connection weights and ANN structure. Experiments performed on benchmark datasets taken from the UCI machine learning repository show that the proposed CSONN-OBD is an effective tool for training neural networks.

  1. HIV lipodystrophy case definition using artificial neural network modelling

    DEFF Research Database (Denmark)

    Ioannidis, John P A; Trikalinos, Thomas A; Law, Matthew;

    2003-01-01

    OBJECTIVE: A case definition of HIV lipodystrophy has recently been developed from a combination of clinical, metabolic and imaging/body composition variables using logistic regression methods. We aimed to evaluate whether artificial neural networks could improve the diagnostic accuracy. METHODS......: The database of the case-control Lipodystrophy Case Definition Study was split into 504 subjects (265 with and 239 without lipodystrophy) used for training and 284 independent subjects (152 with and 132 without lipodystrophy) used for validation. Back-propagation neural networks with one or two middle layers...... were trained and validated. Results were compared against logistic regression models using the same information. RESULTS: Neural networks using clinical variables only (41 items) achieved consistently superior performance than logistic regression in terms of specificity, overall accuracy and area under...

  2. Morphological Classification of Galaxies Using Artificial Neural Networks

    CERN Document Server

    Ball, N M

    2001-01-01

    The results of morphological galaxy classifications performed by humans and by automated methods are compared. In particular, a comparison is made between the eyeball classifications of 454 galaxies in the Sloan Digital Sky Survey (SDSS) commissioning data (Shimasaku et al. 2001) with those of supervised artificial neural network programs constructed using the MATLAB Neural Network Toolbox package. Networks in this package have not previously been used for galaxy classification. It is found that simple neural networks are able to improve on the results of linear classifiers, giving correlation coefficients of the order of 0.8 +/- 0.1, compared with those of around 0.7 +/- 0.1 for linear classifiers. The networks are trained using the resilient backpropagation algorithm, which, to the author's knowledge, has not been specifically used in the galaxy classification literature. The galaxy parameters used and the network architecture are both important, and in particular the galaxy concentration index, a measure o...

  3. RBF neural network based $\\mathcal{H}_{\\infty}$ synchronization for unknown chaotic systems

    Indian Academy of Sciences (India)

    Choon Ki Ahn

    2010-08-01

    In this paper, we propose a new $\\mathcal{H}_{\\infty}$ synchronization strategy, called a Radial Basis Function Neural Network $\\mathcal{H}_{\\infty}$ synchronization (RBFNNHS) strategy, for unknown chaotic systems in the presence of external disturbance. In the proposed framework, a radial basis function neural network (RBFNN) is constructed as an alternative to approximate the unknown nonlinear function of the chaotic system. Based on this neural network and linear matrix inequality (LMI) formulation, the RBFNNHS controller and the learning laws are presented to reduce the effect of disturbance to an $\\mathcal{H}_{\\infty}$ norm constraint. It is shown that finding the RBFNNHS controller and the learning laws can be transformed into the LMI problem and solved using the convex optimization method. A numerical example is presented to demonstrate the validity of the proposed RBFNNHS scheme.

  4. Neural Network Based Algorithm and Simulation of Information Fusion in the Coal Mine

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The concepts of information fusion and the basic principles of neural networks are introduced.Neural networks were introduced as a way of building an information fusion model in a coal mine monitoring system.This assures the accurate transmission of the multi-sensor information that comes from the coal mine monitoring systems.The information fusion mode was analyzed.An algorithm was designed based on this analysis and some simulation results were given.Finally, conclusions that could provide auxiliary decision making information to the coal mine dispatching officers were presented.

  5. Optimization of a neural network based direct inverse control for controlling a quadrotor unmanned aerial vehicle

    OpenAIRE

    Heryanto M Ary; Wahab Wahidin; Kusumoputro Benyamin

    2015-01-01

    UAVs are mostly used for surveillance, inspection and data acquisition. We have developed a Quadrotor UAV that is constructed based on a four motors with a lift-generating propeller at each motors. In this paper, we discuss the development of a quadrotor and its neural networks direct inverse control model using the actual flight data. To obtain a better performance of the control system of the UAV, we proposed an Optimized Direct Inverse controller based on re-training the neural networks wi...

  6. Multiple simultaneous fault diagnosis via hierarchical and single artificial neural networks

    International Nuclear Information System (INIS)

    Process fault diagnosis involves interpreting the current status of the plant given sensor reading and process knowledge. There has been considerable work done in this area with a variety of approaches being proposed for process fault diagnosis. Neural networks have been used to solve process fault diagnosis problems in chemical process, as they are well suited for recognizing multi-dimensional nonlinear patterns. In this work, the use of Hierarchical Artificial Neural Networks in diagnosing the multi-faults of a chemical process are discussed and compared with that of Single Artificial Neural Networks. The lower efficiency of Hierarchical Artificial Neural Networks , in comparison to Single Artificial Neural Networks, in process fault diagnosis is elaborated and analyzed. Also, the concept of a multi-level selection switch is presented and developed to improve the performance of hierarchical artificial neural networks. Simulation results indicate that application of multi-level selection switch increase the performance of the hierarchical artificial neural networks considerably

  7. Neural-network-based two-loop control of robotic manipulators including actuator dynamics in task space

    Institute of Scientific and Technical Information of China (English)

    Liangyong WANG; Tianyou CHAI; Zheng FANG

    2009-01-01

    A neural-network-based motion controller in task space is presented in this paper. The proposed controller is addressed as a two-loop cascade control scheme. The outer loop is given by kinematic control in the task space. It provides a joint velocity reference signal to the inner one. The inner loop implements a velocity servo loop at the robot joint level. A radial basis function network (RBFN) is integrated with proportional-integral (PI) control to construct a velocity tracking control scheme for the inner loop. Finally, a prototype technology based control system is designed for a robotic manipulator. The proposed control scheme is applied to the robotic manipulator. Experimental results confirm the validity of the proposed control scheme by comparing it with other control strategies.

  8. Dynamic Neural Network-Based Pulsed Plasma Thruster (PPT) Fault Detection and Isolation for Formation Flying of Satellites

    Science.gov (United States)

    Valdes, A.; Khorasani, K.

    The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for the Pulsed Plasma Thrusters (PPTs) that are used in the Attitude Control Subsystem (ACS) of satellites that are tasked to perform a formation flying mission. By using data collected from the relative attitudes of the formation flying satellites our proposed "High Level" FDI scheme can detect the pair of thrusters which is faulty, however fault isolation cannot be accomplished. Based on the "High Level" FDI scheme and the DNN-based "Low Level" FDI scheme developed earlier by the authors, an "Integrated" DNN-based FDI scheme is then proposed. To demonstrate the FDI capabilities of the proposed schemes various fault scenarios are simulated.

  9. QUADRATIC PROGRAMMING NEURAL NETWORK BASED INTEGRATED SPACE-TIME INTERFERENCE SUPPRESSION IN CDMA SYSTEMS

    Institute of Scientific and Technical Information of China (English)

    Song Rongfang; Bi Guangguo

    2001-01-01

    Quadratic programming models for integrated space-time interference suppression in CDMA systems are proposed in this paper. The models integrate the advantages of smart antenna and RAKE receiver, mitigate multiuser access interference (MAI) and interchip interference (ICI),and combine multipath components. The zero-forcing conditions are derived. Neural network implementation of the models is also studied.

  10. Classification of Cancer Gene Selection Using Random Forest and Neural Network Based Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Jogendra Kushwah

    2013-06-01

    Full Text Available The free radical gene classification of cancer diseases is challenging job in biomedical data engineering. The improving of classification of gene selection of cancer diseases various classifier are used, but the classification of classifier are not validate. So ensemble classifier is used for cancer gene classification using neural network classifier with random forest tree. The random forest tree is ensembling technique of classifier in this technique the number of classifier ensemble of their leaf node of class of classifier. In this paper we combined neural network with random forest ensemble classifier for classification of cancer gene selection for diagnose analysis of cancer diseases. The proposed method is different from most of the methods of ensemble classifier, which follow an input output paradigm of neural network, where the members of the ensemble are selected from a set of neural network classifier. the number of classifiers is determined during the rising procedure of the forest. Furthermore, the proposed method produces an ensemble not only correct, but also assorted, ensuring the two important properties that should characterize an ensemble classifier. For empirical evaluation of our proposed method we used UCI cancer diseases data set for classification. Our experimental result shows that better result in compression of random forest tree classification.

  11. Neural Networks Based Component Content Soft-Sensor in Countercurrent Rare-Earth Extraction

    Institute of Scientific and Technical Information of China (English)

    杨辉; 谭明皓; 柴天佑

    2003-01-01

    The equilibrium model for multicomponent rare earth extraction is developed using neural networks, which combined with the material balance model could give online prediction of component content in countercurrent rare earth extraction production. Simulation experiments with industrial operation data prove the effect iveness of the hybrid soft- sensor.

  12. Handwritten Pattern Recognition Using Kohonen Neural Network Based on Pixel Character

    Directory of Open Access Journals (Sweden)

    Lulu C. Munggaran

    2014-11-01

    Full Text Available Handwriting is the human way in communicating each other using written media. By the advancement in technology and development of science, there are a lot of changes of technology in terms of communication with computer through handwriting. Therefore, it is needed computer able to receive input in the form of handwriting data and able to recognize the handwriting input. Therefore, this research focuses on handwritten character recognition using Kohonen neural network. The purpose of this research is to find handwriting recognition algorithm which can receive handwriting input and recognize handwritten character directly inputted in computer using Kohonen neural network. This method studies the distribution of a set of patterns without any class information. The basic idea of this technique is understood from how human brain stores images/patterns that have been recognized through eyes, and then able to reveal the images/patterns back. This research has been successful in developing an application to recognize handwritten characters using Kohonen neural network method, and it has been tested. The application is personal computer based and using a canvas as input media. The recognition process consist of 3 stages layer: Input layer, Training Layer and Hidden Layer. The Kohonen neural network method on handwritten character recognition application has good similarity level of character patterns in character mapping process.

  13. Convolutional neural network based sensor fusion for forward looking ground penetrating radar

    Science.gov (United States)

    Sakaguchi, Rayn; Crosskey, Miles; Chen, David; Walenz, Brett; Morton, Kenneth

    2016-05-01

    Forward looking ground penetrating radar (FLGPR) is an alternative buried threat sensing technology designed to offer additional standoff compared to downward looking GPR systems. Due to additional flexibility in antenna configurations, FLGPR systems can accommodate multiple sensor modalities on the same platform that can provide complimentary information. The different sensor modalities present challenges in both developing informative feature extraction methods, and fusing sensor information in order to obtain the best discrimination performance. This work uses convolutional neural networks in order to jointly learn features across two sensor modalities and fuse the information in order to distinguish between target and non-target regions. This joint optimization is possible by modifying the traditional image-based convolutional neural network configuration to extract data from multiple sources. The filters generated by this process create a learned feature extraction method that is optimized to provide the best discrimination performance when fused. This paper presents the results of applying convolutional neural networks and compares these results to the use of fusion performed with a linear classifier. This paper also compares performance between convolutional neural networks architectures to show the benefit of fusing the sensor information in different ways.

  14. Congestion Control for ATM Networks Based on Diagonal Recurrent Neural Networks

    Institute of Scientific and Technical Information of China (English)

    HuangYunxian; YanWei

    1997-01-01

    An adaptive control model and its algorithms based on simple diagonal recurrent neural networks are presented for the dynamic congestion control in broadband ATM networks.Two simple dynamic queuing models of real networks are used to test the performance of the suggested control scheme.

  15. Building an Artificial Idiotopic Immune Model Based on Artificial Neural Network Ideology

    Directory of Open Access Journals (Sweden)

    Hossam Meshref

    2013-01-01

    Full Text Available In the literature, there were many research efforts that utilized the artificial immune networks to model their designed applications, but they were considerably complicated, and restricted to a few areas that such as computer security applications. The objective of this research is to introduce a new model for artificial immune networks that adopts features from other biological successful models to overcome its complexity such as the artificial neural networks. Common concepts between the two systems were investigated to design a simple, yet a robust, model of artificial immune networks. Three artificial neural networks learning models were available to choose from in the research design: supervised, unsupervised, and reinforcement learning models. However, it was found that the reinforcement model is the most suitable model. Research results examined network parameters, and appropriate relations between concentration ranges and their dependent parameters as well as the expected reward during network learning. In conclusion, it is recommended the use of the designed model by other researchers in different applications such as controlling robots in hazardous environment to save human lives as well as using it on image retrieval in general to help the police department identify suspects.

  16. Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm

    Directory of Open Access Journals (Sweden)

    Sudip Mandal

    2016-01-01

    Full Text Available The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

  17. Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm.

    Science.gov (United States)

    Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K

    2016-01-01

    The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process. PMID:26989410

  18. Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm.

    Science.gov (United States)

    Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K

    2016-01-01

    The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

  19. A Hybrid Artificial Neural Network Model for Forecasting Short Time Series

    OpenAIRE

    Mohan, Anil

    2012-01-01

    Forecasting has long been the domain of traditional statistical models. Recent research has shown that novel and complex forecasting models do not necessarily outperform simpler models. These include in particular Artificial Neural Networks (ANNs). Even though claims of superior forecasting performance were made by Neural Network researchers, these claims were often unsubstantiated. Artificial neural networks are information processing paradigms motivated by the information ...

  20. A TWO-LAYER RECURRENT NEURAL NETWORK BASED APPROACH FOR OVERLAY MULTICAST

    Institute of Scientific and Technical Information of China (English)

    Liu Shidong; Zhang Shunyi; Zhou Jinquan; Qiu Gong'an

    2008-01-01

    Overlay multicast has become one of the most promising multicast solutions for IP network, and Neutral Network(NN) has been a good candidate for searching optimal solutions to the constrained shortest routing path in virtue of its powerful capacity for parallel computation. Though traditional Hopfield NN can tackle the optimization problem, it is incapable of dealing with large scale networks due to the large number of neurons. In this paper, a neural network for overlay multicast tree computation is presented to reliably implement routing algorithm in real time. The neural network is constructed as a two-layer recurrent architecture, which is comprised of Independent Variable Neurons (IDVN) and Dependent Variable Neurons (DVN), according to the independence of the decision variables associated with the edges in directed graph. Compared with the heuristic routing algorithms, it is characterized as shorter computational time, fewer neurons, and better precision.

  1. Electricity price forecasting using generalized regression neural network based on principal components analysis

    Institute of Scientific and Technical Information of China (English)

    牛东晓; 刘达; 邢棉

    2008-01-01

    A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.

  2. BP-Neural-Network-Based Tool Wear Monitoring by Using Wavelet Decomposition of the Power Spectrum

    Institute of Scientific and Technical Information of China (English)

    ZHENG Jian-ming; XI Chang-qing; LI Yan; XIAO Ji-ming

    2004-01-01

    In a drilling process, the power spectrum of the drilling force is related to the tool wear and is widely applied in the monitoring of tool wear. But the feature extraction and identification of the power spectrum have always been an unresolved difficult problem. This paper solves it through decomposition of the power spectrum in multilayers using wavelet transform and extraction of the low frequency decomposition coefficient us the envelope information of the power spectrum. Intelligent identification of the tool wear status is achieved in the drilling process through fusing the wavelet decomposition coefficient of the power spectrum by using a BP ( Back Propagation) neural network. The experimental results show that the features of the power spectrum can be extracted efficiently through this method, and the trained neural networks show high identification precision and the ability of extension.

  3. Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Kuo-Nan Yu

    2014-01-01

    Full Text Available At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

  4. A special hierarchical fuzzy neural-networks based reinforcement learning for multi-variables system

    Institute of Scientific and Technical Information of China (English)

    ZHANG Wen-zhi; LU Tian-sheng

    2005-01-01

    Proposes a reinforcement learning scheme based on a special Hierarchical Fuzzy Neural-Networks (HFNN) for solving complicated learning tasks in a continuous multi-variables environment. The output of the previous layer in the HFNN is no longer used as if-part of the next layer, but used only in then-part. Thus it can deal with the difficulty when the output of the previous layer is meaningless or its meaning is uncertain. The proposed HFNN has a minimal number of fuzzy rules and can successfully solve the problem of rules combination explosion and decrease the quantity of computation and memory requirement. In the learning process, two HFNN with the same structure perform fuzzy action composition and evaluation function approximation simultaneously where the parameters of neural-networks are tuned and updated on line by using gradient descent algorithm. The reinforcement learning method is proved to be correct and feasible by simulation of a double inverted pendulum system.

  5. Optimization of a neural network based direct inverse control for controlling a quadrotor unmanned aerial vehicle

    Directory of Open Access Journals (Sweden)

    Heryanto M Ary

    2015-01-01

    Full Text Available UAVs are mostly used for surveillance, inspection and data acquisition. We have developed a Quadrotor UAV that is constructed based on a four motors with a lift-generating propeller at each motors. In this paper, we discuss the development of a quadrotor and its neural networks direct inverse control model using the actual flight data. To obtain a better performance of the control system of the UAV, we proposed an Optimized Direct Inverse controller based on re-training the neural networks with the new data generated from optimal maneuvers of the quadrotor. Through simulation of the quadrotor using the developed DIC and Optimized DIC model, results show that both models have the ability to stabilize the quadrotor with a good tracking performance. The optimized DIC model, however, has shown a better performance, especially in the settling time parameter.

  6. NEURAL NETWORK BASED OPTIMISATION OF HARMONICS ON AC - DC SIDES OF HVDC TRANSMISSION

    OpenAIRE

    Smriti Tapadar; Tikeshwar Gajpal

    2015-01-01

    This paper investigates possibility of using Neural network for optimisation of AC - DC Harmonics of HVDC Transmission. Dependableness and potency of power transmission has been at the forefront of analysis for a few time and is presently being given essential thought owing to the inflated dependence on current. With the inflated demand for e lectricity, engineers area unit considering completely different strategies of provide arrangement to enhance the ...

  7. Multilingual Deep Neural Network based Acoustic Modeling For Rapid Language Adaptation

    OpenAIRE

    Vu, Ngoc Thang; Imseng, David; Povey, Daniel; Motlicek, Petr; Schultz, Tanja; Bourlard, Hervé

    2014-01-01

    This paper presents a study on multilingual deep neural network (DNN) based acoustic modeling and its application to new languages. We investigate the effect of phone merging on multilingual DNN in context of rapid language adaptation. Moreover, the combination of multilingual DNNs with Kullback--Leibler divergence based acoustic modeling (KL-HMM) is explored. Using ten different languages from the Globalphone database, our studies reveal that crosslingual acoustic model transfer through mul...

  8. Neural networks based neutron emissivity tomography at JET with real-time capabilities

    International Nuclear Information System (INIS)

    Tomographic reconstruction techniques typically require computationally intensive algorithms which are not suitable for real-time application. This paper describes a framework to perform neutron emissivity tomography at the Joint European Torus (JET) using neural networks with successful results over a broad range of magnetic configurations, heating and fueling schemes. Application times in the μs time scale allows for real-time applicability of the method.

  9. Handwritten Pattern Recognition Using Kohonen Neural Network Based on Pixel Character

    OpenAIRE

    Lulu C. Munggaran; Suryarini Widodo; Cipta A.M; Nuryuliani

    2014-01-01

    Handwriting is the human way in communicating each other using written media. By the advancement in technology and development of science, there are a lot of changes of technology in terms of communication with computer through handwriting. Therefore, it is needed computer able to receive input in the form of handwriting data and able to recognize the handwriting input. Therefore, this research focuses on handwritten character recognition using Kohonen neural network. The purpose of this rese...

  10. A New Training Method for Feedforward Neural Networks Based on Geometric Contraction Property of Activation Functions

    OpenAIRE

    Birtea, Petre; Cernazanu-Glavan, Cosmin; Sisu, Alexandru

    2016-01-01

    We propose a new training method for a feedforward neural network having the activation functions with the geometric contraction property. The method consists of constructing a new functional that is less nonlinear in comparison with the classical functional by removing the nonlinearity of the activation functions from the output layer. We validate this new method by a series of experiments that show an improved learning speed and also a better classification error.

  11. Neural networks based neutron emissivity tomography at JET with real-time capabilities

    Energy Technology Data Exchange (ETDEWEB)

    Ronchi, E., E-mail: emanuele.ronchi@tsl.uu.s [JET-EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Association EURATOM-VR, Department of Physics and Astronomy, Uppsala University, SE-75120 Uppsala (Sweden); Conroy, S.; Andersson Sunden, E.; Ericsson, G.; Gatu Johnson, M.; Hellesen, C.; Sjoestrand, H.; Weiszflog, M. [JET-EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Association EURATOM-VR, Department of Physics and Astronomy, Uppsala University, SE-75120 Uppsala (Sweden)

    2010-02-01

    Tomographic reconstruction techniques typically require computationally intensive algorithms which are not suitable for real-time application. This paper describes a framework to perform neutron emissivity tomography at the Joint European Torus (JET) using neural networks with successful results over a broad range of magnetic configurations, heating and fueling schemes. Application times in the mus time scale allows for real-time applicability of the method.

  12. Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection

    OpenAIRE

    Kang Xie; Yixian Yang; Yang Xin; Guangsheng Xia

    2015-01-01

    According to the problems of current distributed architecture intrusion detection systems (DIDS), a new online distributed intrusion detection model based on cellular neural network (CNN) was proposed, in which discrete-time CNN (DTCNN) was used as weak classifier in each local node and state-controlled CNN (SCCNN) was used as global detection method, respectively. We further proposed a new method for design template parameters of SCCNN via solving Linear Matrix Inequality. Experimental resul...

  13. Taste Identification of Tea Through a Fuzzy Neural Network Based on Fuzzy C-means Clustering

    Institute of Scientific and Technical Information of China (English)

    ZHENG Yan; ZHOU Chun-guang

    2003-01-01

    In this paper, we present a fuzzy neural network model based on Fuzzy C-Means (FCM) clustering algorithm to realize the taste identification of tea. The proposed method can acquire the fuzzy subset and its membership function in an automatic way with the aid of FCM clustering algorithm. Moreover, we improve the fuzzy weighted inference approach. The proposed model is illustrated with the simulation of taste identification of tea.

  14. Particle Swarm Optimization Recurrent Neural Network Based Z-source Inverter Fed Induction Motor Drive

    OpenAIRE

    R. Selva Santhose Kumar; S.M. Girirajkumar

    2014-01-01

    In this study, the proposal is made for Particle Swarm Optimization (PSO) Recurrent Neural Network (RNN) based Z-Source Inverter Fed Induction Motor Drive. The proposed method is used to enhance the performance of the induction motor while reducing the Total Harmonic Distortion (THD), eliminating the oscillation period of the stator current, torque and speed. Here, the PSO technique uses the induction motor speed and reference speed as the input parameters. From the input parameters, it optim...

  15. BP Artificial Neural Network- Based Rural Human Settlement Environment Assessment at Three Karst Areas in Guizhou Province%基于BP神经网络的贵州3个喀斯特农村地区人居环境评价

    Institute of Scientific and Technical Information of China (English)

    周晓芳; 周永章; 欧阳军

    2012-01-01

    To evaluate the rural human settlement in Karst landform region of Guizhou Province,-three typical areas , Hongfeng community at Qinzhen city, Yachi community at Bijie city and Huajiang community at the junction of Zhenfeng county and Guarding county are selected as the examples. Firstly, assessment index system for actual environmental conditions in Karst is established on the basis of the existing theory and practice in human settlement e-valuation. Then the statistic characters about socio - economic survey data of 29 administrative villages and the structure of ANN is combined to build and train a reasonable neural network. Lastly, the BP ANN is used to assess them. Results show that Hongfeng had better human settlement communities, most of Yachi and Huajiang resident level is in the mid or lower grade, and the worst assessment is in Huanjiang. Therefore, rural living environment in Karst area closely related to regional geographic environment, especially natural conditions, and it is still the key for Karst human settlement.%选取具有代表性的贵州省清镇红枫区、毕节鸭池区以及关岭-贞丰花江区作为研究对象,在分析喀斯特人居环境研究现状以及人居环境评价理论和实践的基础上,结合喀斯特地区的实际环境条件构建宜居指数,对喀斯特农村的村落人居环境进行评价.构建并训练得到合理的神经网络,评价结果显示,居住环境最好和较好的村落以红枫区最多,鸭池和花江区的村落大部分处于中下水平,且花江区的部分村落评价结果最差.说明喀斯特地区人居环境与区域综合地理环境和自然条件密切相关,自然环境仍然是喀斯特地区人居环境的主导因素.

  16. Diagnostic Classification of Normal Persons and Cancer Patients by Using Neural Network Based on Trace Metal Contents in Serum Samples

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Artificial neural network with the back-propagation(BP-ANN) approach was applied to the classification of normal persons and various cancer patients based on the elemental contents in serum samples. This method was verified by the cross-validation method. The effects of the net work parameters were investigated and the related problems were discussed. The samples of 72, 42, and 52 for lung, liver, and stomach cancer patients and normal persons, respectively, were used for the classification study. About 95% of the samples can be classified correctly. There fore, the method can be used as an auxiliary means of the diagnosis of cancer.

  17. RECOGNITION OF CDNA MICROARRAY IMAGE USING FEEDFORWARD ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    R. M. Farouk

    2014-07-01

    Full Text Available The complementary DNA (cDNA sequence considered th e magic biometric technique for personal identification. Microarray image processing used fo r the concurrent genes identification. In this pape r, we present a new method for cDNA recognition based on the artificial neural network (ANN. We have segmented the location of the spots in a cDNA micro array. Thus, a precise localization and segmenting of a spot are essential to obtain a more exact intensity measurement, leading to a more accurate gene expression measurement. The segmented cDNA microarr ay image resized and used as an input for the proposed artificial neural network. For matching an d recognition, we have trained the artificial neura l network. Recognition results are given for the gall eries of cDNA sequences . The numerical results sho w that, the proposed matching technique is an effecti ve in the cDNA sequences process. The experimental results of our matching approach using different da tabases shows that, the proposed technique is an effective matching performance.

  18. A Fuzzy Neural Network Based on Non-Euclidean Distance Clustering for Quality Index Model in Slashing Process

    Directory of Open Access Journals (Sweden)

    Yuxian Zhang

    2015-01-01

    Full Text Available The quality index model in slashing process is difficult to build by reason of the outliers and noise data from original data. To the above problem, a fuzzy neural network based on non-Euclidean distance clustering is proposed in which the input space is partitioned into many local regions by the fuzzy clustering based on non-Euclidean distance so that the computation complexity is decreased, and fuzzy rule number is determined by validity function based on both the separation and the compactness among clusterings. Then, the premise parameters and consequent parameters are trained by hybrid learning algorithm. The parameters identification is realized; meanwhile the convergence condition of consequent parameters is obtained by Lyapunov function. Finally, the proposed method is applied to build the quality index model in slashing process in which the experimental data come from the actual slashing process. The experiment results show that the proposed fuzzy neural network for quality index model has lower computation complexity and faster convergence time, comparing with GP-FNN, BPNN, and RBFNN.

  19. Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete

    OpenAIRE

    Sangyong Kim; Hee-Bok Choi; Yoonseok Shin; Gwang-Hee Kim; Deok-Seok Seo

    2013-01-01

    This research aims to optimize the mixing proportion of recycled aggregate concrete (RAC) using neural networks (NNs) based on genetic algorithms (GAs) for increasing the use of recycled aggregate (RA). NN and GA were used to predict the compressive strength of the concrete at 28 days. And sensitivity analysis of the NN based on GA was used to find the mixing ratio of RAC. The mixing criteria for RAC were determined and the replacement ratio of RAs was identified. This research reveal that th...

  20. Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

    Directory of Open Access Journals (Sweden)

    Tomasz Ciszewski

    2006-01-01

    Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

  1. Feedforward, high density, programmable read only neural network based memory system

    Science.gov (United States)

    Daud, Taher; Moopenn, Alex; Lamb, James; Thakoor, Anil; Khanna, Satish

    1988-01-01

    Neural network-inspired, nonvolatile, programmable associative memory using thin-film technology is demonstrated. The details of the architecture, which uses programmable resistive connection matrices in synaptic arrays and current summing and thresholding amplifiers as neurons, are described. Several synapse configurations for a high-density array of a binary connection matrix are also described. Test circuits are evaluated for operational feasibility and to demonstrate the speed of the read operation. The results are discussed to highlight the potential for a read data rate exceeding 10 megabits/sec.

  2. Neural network based neutral particles reconstruction with the E687 hadron calorimeter

    Science.gov (United States)

    Arena, V.; Boca, G.; Bonomi, G.; Gérard, G.; Gianini, G.; Marchesotti, M.; Merlo, M.; Ratti, S. P.; Riccardi, C.; Viola, L.; Vitulo, P.; Buchholz, D.; Claes, D.; O'Reilly, B.

    1996-02-01

    We present a neutral particle reconstruction algorithm based on a neural network approach applied to the E687 hadron calorimeter. A measurement of the invariant mass of the Σ± → nπ± is presented to verify the reliability of the reconstruction. The reconstructed invariant mass of the charmed meson D + → K L0π+π+π- is also presented to show the possible application of this technique to charmed particles decaying into a neutral hadron. An example of this would be Λc+ → nK -π+π+.

  3. Multiple-model-and-neural-network-based nonlinear multivariable adaptive control

    Institute of Scientific and Technical Information of China (English)

    Yue FU; Tianyou CHAI

    2007-01-01

    A multivariable adaptive controller feasible for implementation on distributed computer systems (DCS) is presented for a class of uncertain nonlinear multivariable discrete time systems. The adaptive controller is composed of a linear adaptive controller, a neural network nonlinear adaptive controller and a switching mechanism. The linear controller can provide boundedness of the input and output signals, and the nonlinear controller can improve the performance of the system. The purpose of using the switching mechanism is to obtain the improved system performance and stability simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.

  4. Hour-Glass Neural Network Based Daily Money Flow Estimation for Automatic Teller Machines

    Science.gov (United States)

    Karungaru, Stephen; Akashi, Takuya; Nakano, Miyoko; Fukumi, Minoru

    Monetary transactions using Automated Teller Machines (ATMs) have become a normal part of our daily lives. At ATMs, one can withdraw, send or debit money and even update passbooks among many other possible functions. ATMs are turning the banking sector into a ubiquitous service. However, while the advantages for the ATM users (financial institution customers) are many, the financial institution side faces an uphill task in management and maintaining the cash flow in the ATMs. On one hand, too much money in a rarely used ATM is wasteful, while on the other, insufficient amounts would adversely affect the customers and may result in a lost business opportunity for the financial institution. Therefore, in this paper, we propose a daily cash flow estimation system using neural networks that enables better daily forecasting of the money required at the ATMs. The neural network used in this work is a five layered hour glass shaped structure that achieves fast learning, even for the time series data for which seasonality and trend feature extraction is difficult. Feature extraction is carried out using the Akamatsu Integral and Differential transforms. This work achieves an average estimation accuracy of 92.6%.

  5. Cellular neural network-based hybrid approach toward automatic image registration

    Science.gov (United States)

    Arun, Pattathal VijayaKumar; Katiyar, Sunil Kumar

    2013-01-01

    Image registration is a key component of various image processing operations that involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however, inability to properly model object shape as well as contextual information has limited the attainable accuracy. A framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as vector machines, cellular neural network (CNN), scale invariant feature transform (SIFT), coreset, and cellular automata is proposed. CNN has been found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using coreset optimization. The salient features of this work are cellular neural network approach-based SIFT feature point optimization, adaptive resampling, and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. This system has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. This methodology is also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

  6. Learning algorithm and application of quantum BP neural networks based on universal quantum gates

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    A quantum BP neural networks model with learning algorithm is proposed.First,based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate,a quantum neuron model is constructed,which is composed of input,phase rotation,aggregation,reversal rotation and output.In this model,the input is described by qubits,and the output is given by the probability of the state in which |1> is observed.The phase rotation and the reversal rotation are performed by the universal quantum gates.Secondly,the quantum BP neural networks model is constructed,in which the output layer and the hide layer are quantum neurons.With the application of the gradient descent algorithm,a learning algorithm of the model is proposed,and the continuity of the model is proved.It is shown that this model and algorithm are superior to the conventional BP networks in three aspects: convergence speed,convergence rate and robustness,by two application examples of pattern recognition and function approximation.

  7. BP neural network based online prediction of steam turbine exhaust dryness

    Institute of Scientific and Technical Information of China (English)

    XIE Danmei; CHEN Chang; XIONG Yangheng; GAO Shang; WANG Chun

    2014-01-01

    In large scale condensing turbine unit,the exhaust status always lies in wet steam area.Due to the lack of effective measuring method,the exhaust dryness of the steam turbine is difficult to obtain di-rectly,which has been the difficult problem in online economic analysis for thermal power units.By taking an N1000-25/600/600 ultra-supercritical steam turbine as an example,the nonlinear mapping ability of BP neural network was used to establish a model which can reflect the relationship between exhaust dryness and unit load and exhaust pressure.After learning and training under some typical conditions,this model was used for exhaust dryness online calculation under full condition.The results show the final error of the training samples and verifying samples were controlled within -0.006 1 and -0.001 0,which satisfies the accuracy requirement for engineering calculation,indicating the established BP neural network can be used in exhaust dryness prediction.

  8. Monitoring the Freshness of Moroccan Sardines with a Neural-Network Based Electronic Nose

    Directory of Open Access Journals (Sweden)

    Benachir Bouchikhi

    2006-10-01

    Full Text Available An electronic nose was developed and used as a rapid technique to classify thefreshness of sardine samples according to the number of days spent under cold storage (4 ±1°C, in air. The volatile compounds present in the headspace of weighted sardine sampleswere introduced into a sensor chamber and the response signals of the sensors wererecorded as a function of time. Commercially available gas sensors based on metal oxidesemiconductors were used and both static and dynamic features from the sensorconductance response were input to the pattern recognition engine. Data analysis wasperformed by three different pattern recognition methods such as probabilistic neuralnetworks (PNN, fuzzy ARTMAP neural networks (FANN and support vector machines(SVM. The objective of this study was to find, among these three pattern recognitionmethods, the most suitable one for accurately identifying the days of cold storage undergoneby sardine samples. The results show that the electronic nose can monitor the freshness ofsardine samples stored at 4°C, and that the best classification and prediction are obtainedwith SVM neural network. The SVM approach shows improved classificationperformances, reducing the amount of misclassified samples down to 3.75 %.

  9. A genetic-neural artificial intelligence approach to resins optimization

    International Nuclear Information System (INIS)

    This work presents a preliminary study about the viability and adequacy of a new methodology for the definition of one of the main properties of ion exchange resins used for isotopic separation. Basically, the main problem is the definition of pelicule diameter in case of pelicular ion exchange resins, in order to achieve the best performance in the shortest time. In order to achieve this, a methodology was developed, based in two classic techniques of Artificial Intelligence (AI). At first, an artificial neural network (NN) was trained to map the existing relations between the nucleus radius and the resin's efficiency associated with the exchange time. Later on, a genetic algorithm (GA) was developed in order to find the best pelicule dimension. Preliminary results seem to confirm the potential of the method, and this can be used in any chemical process employing ion exchange resins. (author)

  10. Fault Tolerant Characteristics of Artificial Neural Network Electronic Hardware

    Science.gov (United States)

    Zee, Frank

    1995-01-01

    The fault tolerant characteristics of analog-VLSI artificial neural network (with 32 neurons and 532 synapses) chips are studied by exposing them to high energy electrons, high energy protons, and gamma ionizing radiations under biased and unbiased conditions. The biased chips became nonfunctional after receiving a cumulative dose of less than 20 krads, while the unbiased chips only started to show degradation with a cumulative dose of over 100 krads. As the total radiation dose increased, all the components demonstrated graceful degradation. The analog sigmoidal function of the neuron became steeper (increase in gain), current leakage from the synapses progressively shifted the sigmoidal curve, and the digital memory of the synapses and the memory addressing circuits began to gradually fail. From these radiation experiments, we can learn how to modify certain designs of the neural network electronic hardware without using radiation-hardening techniques to increase its reliability and fault tolerance.

  11. An Artificial Neural Network for Data Forecasting Purposes

    Directory of Open Access Journals (Sweden)

    Catalina Lucia COCIANU

    2015-01-01

    Full Text Available Considering the fact that markets are generally influenced by different external factors, the stock market prediction is one of the most difficult tasks of time series analysis. The research reported in this paper aims to investigate the potential of artificial neural networks (ANN in solving the forecast task in the most general case, when the time series are non-stationary. We used a feed-forward neural architecture: the nonlinear autoregressive network with exogenous inputs. The network training function used to update the weight and bias parameters corresponds to gradient descent with adaptive learning rate variant of the backpropagation algorithm. The results obtained using this technique are compared with the ones resulted from some ARIMA models. We used the mean square error (MSE measure to evaluate the performances of these two models. The comparative analysis leads to the conclusion that the proposed model can be successfully applied to forecast the financial data.

  12. Design of Jetty Piles Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Yongjei Lee

    2014-01-01

    Full Text Available To overcome the complication of jetty pile design process, artificial neural networks (ANN are adopted. To generate the training samples for training ANN, finite element (FE analysis was performed 50 times for 50 different design cases. The trained ANN was verified with another FE analysis case and then used as a structural analyzer. The multilayer neural network (MBPNN with two hidden layers was used for ANN. The framework of MBPNN was defined as the input with the lateral forces on the jetty structure and the type of piles and the output with the stress ratio of the piles. The results from the MBPNN agree well with those from FE analysis. Particularly for more complex modes with hundreds of different design cases, the MBPNN would possibly substitute parametric studies with FE analysis saving design time and cost.

  13. XDANNG: XML based Distributed Artificial Neural Network with Globus Toolkit

    CERN Document Server

    Mahini, Hamidreza; Ghofrani, Javad

    2009-01-01

    Artificial Neural Network is one of the most common AI application fields. This field has direct and indirect usages most sciences. The main goal of ANN is to imitate biological neural networks for solving scientific problems. But the level of parallelism is the main problem of ANN systems in comparison with biological systems. To solve this problem, we have offered a XML-based framework for implementing ANN on the Globus Toolkit Platform. Globus Toolkit is well known management software for multipurpose Grids. Using the Grid for simulating the neuron network will lead to a high degree of parallelism in the implementation of ANN. We have used the XML for improving flexibility and scalability in our framework.

  14. Arabic Named Entity Recognition Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Naji F. Mohammed

    2012-01-01

    Full Text Available Problem statement: Named Entity Recognition (NER is a task to identify proper names as well as temporal and numeric expressions, in an open-domain text. The NER task can help to improve the performance of various Natural Language Processing (NLP applications such as Information Extraction (IE, Information Retrieval (IR and Question Answering (QA tasks. This study discusses on the Named Entity Recognition of Arabic (NERA. The motivation is due to the lack of resources for Arabic named entities and to enhance the accuracy that has been reached in previous NERA systems. Approach: This system is designed based on neural network approach. The main task of neural network approach is to automatically learn to recognize component patterns and make intelligent decisions based on available data and it can also be applied to classify new information within large databases. The use of machine learning approach to classify NER from Arabic text based on neural network technique is proposed. Neural network approach has performed successfully in many areas of artificial intelligence. The system involves three stages: the first stage is pre-processing that cleans the collected data, the second involves converting Arabic letters to Roman alphabets and the final stage applies neural network to classify the collected data. Results: The accuracy of the system is 92 %. The system is compared with decision tree using the same data. The results showed that the neural network approach achieved better than decision tree. Conclusion: These results prove that our technique is capable to recognize named entities of Arabic texts.

  15. Product Assembly Cost Estimation Based on Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper proposes a method for assembly cost estimation in actual manufacture during the design phase using artificial neural networks (ANN). It can support the de signers in cost effectiveness, then help to control the total cost. The method was used in the assembly cost estimation of the crucial parts of some railway stock products. As a compari son, we use the linear regression (LR) model in the same field. The result shows that ANN model performs better than the LR model in assembly cost estimation.

  16. Artificial Neural Networks for Dairy Industry: A Review

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2012-06-01

    Full Text Available Artificial Neural Networks (ANNs have been implemented in almost every field, viz., science, technology, medicine and engineering as they have proved useful tools for obtaining the desired output including the analyses and shelf life prediction in case of food products. This review discusses the systematic information available in the literature concerning the implementation of ANN models for shelf life determination of milk, various dairy products, viz., yoghurt, chocolate, butter, cheese, kalakand, dairy confections and milk based coffee drinks, which would be very useful for dairy industry, consumers, wholesalers, retailers, regulatory agencies, food researchers and academicians.

  17. Artificial Neural Network Model of Hydrocarbon Migration and Accumulation

    Institute of Scientific and Technical Information of China (English)

    刘海滨; 吴冲龙

    2002-01-01

    Based on the dynamic simulation of the 3-D structure the sedimentary modeling, the unit entity model has been adopted to transfer the heterogeneous complex pas sage system into limited simple homogeneous entity, and then the traditional dyn amic simulation has been used to calculate the phase and the drive forces of the hyd rocarbon , and the artificial neural network(ANN) technology has been applied to resolve such problems as the direction, velocity and quantity of the hydrocarbo n migration among the unit entities. Through simulating of petroleum migration a nd accumulation in Zhu Ⅲ depression, the complex mechanism of hydrocarbon migra tion and accumulation has been opened out.

  18. Inflow forecasting using Artificial Neural Networks for reservoir operation

    OpenAIRE

    Chiamsathit, Chuthamat; Adeloye, Adebayo J.; Bankaru-Swamy, Soundharajan

    2016-01-01

    In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the ...

  19. A Neuron- and a Synapse Chip for Artificial Neural Networks

    OpenAIRE

    Lansner, John; Lehmann, Torsten

    1992-01-01

    A cascadable, analog, CMOS chip set has been developed for hardware implementations of artificial neural networks (ANN's):I) a neuron chip containing an array of neurons with hyperbolic tangent activation functions and adjustable gains, and II) a synapse chip (or a matrix-vector multiplier) where the matrix is stored on-chip as differential voltages on capacitors. In principal any ANN configuration can be made using these chips. A neuron array of 4 neurons and a 4 × 4 matrix-vector multiplie...

  20. Discrimination between earthquakes and chemical explosions using artificial neural networks

    International Nuclear Information System (INIS)

    An Artificial Neural Network (ANN) for discriminating between earthquakes and chemical explosions located at epicentral distances, Δ <5 deg from Gauribidanur Array (GBA) has been developed using the short period digital seismograms recorded at GBA. For training the ANN spectral amplitude ratios between P and Lg phases computed at 13 different frequencies in the frequency range of 2-8 Hz, corresponding to 20 earthquakes and 23 chemical explosions were used along with other parameters like magnitude, epicentral distance and amplitude ratios Rg/P and Rg/Lg. After training and development, the ANN has correctly identified a set of 21 test events, comprising 6 earthquakes and 15 chemical explosions. (author)

  1. Accuracy Driven Artificial Neural Networks in Stock Market Prediction

    Directory of Open Access Journals (Sweden)

    Selvan Simon

    2012-06-01

    Full Text Available Globalization has made the stock market prediction (SMP accuracy more challenging and rewarding for the researchers and other participants in the stock market. Local and global economic situations alongwith the company’s financial strength and prospects have to be taken into account to improve the prediction accuracy. Artificial Neural Networks (ANN has been identified to be one of the dominant data mining techniques in stock market prediction area. In this paper, we survey different ANN models that have been experimented in SMP with the special enhancement techniques used with them to improve the accuracy. Also, we explore the possible research strategies in this accuracy driven ANN models.

  2. Natural and artificial intelligence misconceptions about brains and neural networks

    CERN Document Server

    de Callataÿ, A

    1992-01-01

    How does the mind work? How is data stored in the brain? How does the mental world connect with the physical world? The hybrid system developed in this book shows a radically new view on the brain. Briefly, in this model memory remains permanent by changing the homeostasis rebuilding the neuronal organelles. These transformations are approximately abstracted as all-or-none operations. Thus the computer-like neural systems become plausible biological models. This illustrated book shows how artificial animals with such brains learn invariant methods of behavior control from their repeated action

  3. Artificial Neural Networks for Detection of Malaria in RBCs

    CERN Document Server

    Pandit, Purnima

    2016-01-01

    Malaria is one of the most common diseases caused by mosquitoes and is a great public health problem worldwide. Currently, for malaria diagnosis the standard technique is microscopic examination of a stained blood film. We propose use of Artificial Neural Networks (ANN) for the diagnosis of the disease in the red blood cell. For this purpose features / parameters are computed from the data obtained by the digital holographic images of the blood cells and is given as input to ANN which classifies the cell as the infected one or otherwise.

  4. Hybrid multiobjective evolutionary design for artificial neural networks.

    Science.gov (United States)

    Goh, Chi-Keong; Teoh, Eu-Jin; Tan, Kay Chen

    2008-09-01

    Evolutionary algorithms are a class of stochastic search methods that attempts to emulate the biological process of evolution, incorporating concepts of selection, reproduction, and mutation. In recent years, there has been an increase in the use of evolutionary approaches in the training of artificial neural networks (ANNs). While evolutionary techniques for neural networks have shown to provide superior performance over conventional training approaches, the simultaneous optimization of network performance and architecture will almost always result in a slow training process due to the added algorithmic complexity. In this paper, we present a geometrical measure based on the singular value decomposition (SVD) to estimate the necessary number of neurons to be used in training a single-hidden-layer feedforward neural network (SLFN). In addition, we develop a new hybrid multiobjective evolutionary approach that includes the features of a variable length representation that allow for easy adaptation of neural networks structures, an architectural recombination procedure based on the geometrical measure that adapts the number of necessary hidden neurons and facilitates the exchange of neuronal information between candidate designs, and a microhybrid genetic algorithm ( microHGA) with an adaptive local search intensity scheme for local fine-tuning. In addition, the performances of well-known algorithms as well as the effectiveness and contributions of the proposed approach are analyzed and validated through a variety of data set types.

  5. Artificial neural nets application in the cotton yarn industry

    Directory of Open Access Journals (Sweden)

    Gilberto Clóvis Antoneli

    2016-06-01

    Full Text Available The competitiveness in the yarn production sector has led companies to search for solutions to attain quality yarn at a low cost. Today, the difference between them, and thus the sector, is in the raw material, meaning processed cotton and its characteristics. There are many types of cotton with different characteristics due to its production region, harvest, storage and transportation. Yarn industries work with cotton mixtures, which makes it difficult to determine the quality of the yarn produced from the characteristics of the processed fibers. This study uses data from a conventional spinning, from a raw material made of 100% cotton, and presents a solution with artificial neural nets that determine the thread quality information, using the fibers’ characteristics values and settings of some process adjustments. In this solution a neural net of the type MultiLayer Perceptron with 11 entry neurons (8 characteristics of the fiber and 3 process adjustments, 7 output neurons (yarn quality and two types of training, Back propagation and Conjugate gradient descent. The selection and organization of the production data of the yarn industry of the cocamar® indústria de fios company are described, to apply the artificial neural nets developed. In the application of neural nets to determine yarn quality, one concludes that, although the ideal precision of absolute values is lacking, the presented solution represents an excellent tool to define yarn quality variations when modifying the raw material composition. The developed system enables a simulation to define the raw material percentage mixture to be processed in the plant using the information from the stocked cotton packs, thus obtaining a mixture that maintains the stability of the entire productive process.

  6. Comparison of Regression and Artificial Neural Network Models for Surface Roughness Prediction with the Cutting Parameters in CNC Turning

    Directory of Open Access Journals (Sweden)

    Muammer Nalbant

    2007-06-01

    Full Text Available Surface roughness, an indicator of surface quality, is one of the most specified customer requirements in machining of parts. In this study, the experimental results corresponding to the effects of different insert nose radii of cutting tools (0.4, 0.8, 1.2 mm, various depth of cuts (0.75, 1.25, 1.75, 2.25, 2.75 mm, and different feedrates (100, 130, 160, 190, 220 mm/min on the surface quality of the AISI 1030 steel workpieces have been investigated using multiple regression analysis and artificial neural networks (ANN. Regression analysis and neural network-based models used for the prediction of surface roughness were compared for various cutting conditions in turning. The data set obtained from the measurements of surface roughness was employed to and tests the neural network model. The trained neural network models were used in predicting surface roughness for cutting conditions. A comparison of neural network models with regression model was carried out. Coefficient of determination was 0.98 in multiple regression model. The scaled conjugate gradient (SCG model with 9 neurons in hidden layer has produced absolute fraction of variance (R2 values of 0.999 for the training data, and 0.998 for the test data. Predictive neural network model showed better predictions than various regression models for surface roughness. However, both methods can be used for the prediction of surface roughness in turning.

  7. Artificial neural networks application in duplex/triplex elevator group control system:

    OpenAIRE

    Imrak, C. Erdem

    2008-01-01

    Artificial neural networks can offer the better solution to the passenger call distribution problem when compared to the conventional elevator control systems. Therefore, the application of neural networks in elevator group control system is discussed. The significance of introducing artificial neural networks is presented. Elevator group control systems with neural networks can predict the next stopping floors to stop by considering what has been learnt by processing the changes in passenger...

  8. Neural network-based adaptive consensus tracking control for multi-agent systems under actuator faults

    Science.gov (United States)

    Zhao, Lin; Jia, Yingmin

    2016-06-01

    In this paper, a distributed output feedback consensus tracking control scheme is proposed for second-order multi-agent systems in the presence of uncertain nonlinear dynamics, external disturbances, input constraints, and partial loss of control effectiveness. The proposed controllers incorporate reduced-order filters to account for the unmeasured states, and the neural networks technique is implemented to approximate the uncertain nonlinear dynamics in the synthesis of control algorithms. In order to compensate the partial loss of actuator effectiveness faults, fault-tolerant parts are included in controllers. Using the Lyapunov approach and graph theory, it is proved that the controllers guarantee a group of agents that simultaneously track a common time-varying state of leader, even when the state of leader is available only to a subset of the members of a group. Simulation results are provided to demonstrate the effectiveness of the proposed consensus tracking method.

  9. Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm

    Directory of Open Access Journals (Sweden)

    Qing Han

    2013-10-01

    Full Text Available Emergency rescues require that first responders provide support to evacuate injured and other civilians who are obstructed by the hazards. In this case, the emergency personnel can take actions strategically in order to rescue people maximally, efficiently and quickly. The paper studies the effectiveness of a random neural network (RNN-based task assignment algorithm involving optimally matching emergency personnel and injured civilians, so that the emergency personnel can aid trapped people to move towards evacuation exits in real-time. The evaluations are run on a decision support evacuation system using the Distributed Building Evacuation Simulator (DBES multi-agent platform in various emergency scenarios. The simulation results indicate that the RNN-based task assignment algorithm provides a near-optimal solution to resource allocation problems, which avoids resource wastage and improves the efficiency of the emergency rescue process.

  10. Neural network-based optimal adaptive output feedback control of a helicopter UAV.

    Science.gov (United States)

    Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

    2013-07-01

    Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

  11. An Unsupervised Dynamic Image Segmentation using Fuzzy Hopfield Neural Network based Genetic Algorithm

    CERN Document Server

    Halder, Amiya

    2012-01-01

    This paper proposes a Genetic Algorithm based segmentation method that can automatically segment gray-scale images. The proposed method mainly consists of spatial unsupervised grayscale image segmentation that divides an image into regions. The aim of this algorithm is to produce precise segmentation of images using intensity information along with neighborhood relationships. In this paper, Fuzzy Hopfield Neural Network (FHNN) clustering helps in generating the population of Genetic algorithm which there by automatically segments the image. This technique is a powerful method for image segmentation and works for both single and multiple-feature data with spatial information. Validity index has been utilized for introducing a robust technique for finding the optimum number of components in an image. Experimental results shown that the algorithm generates good quality segmented image.

  12. Dynamic Bandwidth Allocation Technique in ATM Networks Based on Fuzzy Neural Networks and Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    ZhangLiangjie; LiYanda; 等

    1997-01-01

    In this paper,a dynamic bandwidth allocation technique based on fuzz neural networks(FNNs) and genetic algorithm(GA)is proposed for preventive congestion control in ATM network.The traffic model based on FNN does not need the descriptive traffic parameters in detail,which greatly depend on the user's terminal.Genetic algorithm is used to predict the equivalent bandwidth of the accepted traffic in real-time.Thus,the proposed scheme can estimate the dynamic bandwidth of the network in the time scale from the call arrival to the call admission/rejection due to the fuzzy-tech and GA hardware implementation.Simulation results show that the scheme can perform accurate dynamic bandwidth allocation to DN/OFF bursty traffic in accordance with the required quality of service(QOS),and the bandwidth utilization is improved from the overall point of view.

  13. Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification.

    Science.gov (United States)

    Sarkar, Sankho Turjo; Bhondekar, Amol P; Macaš, Martin; Kumar, Ritesh; Kaur, Rishemjit; Sharma, Anupma; Gulati, Ashu; Kumar, Amod

    2015-11-01

    The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis. PMID:26356597

  14. Radial Basis Function Neural Networks Based QSPR for the Prediction of log P

    Institute of Scientific and Technical Information of China (English)

    YAO,Xiao-Jun(姚小军); LIU,Man-Cang(刘满仓); ZHANG,Xiao-Yun(张晓昀); ZHANG,Rui-Sheng(张瑞生); HU,Zhi-De(胡之德); FAN,Bo-Tao(范波涛)

    2002-01-01

    Quantitative structure-property relatioonship (QSPR) method is used to study the correlation models between the structures of a set of diverse organic compounds and their log P. Molecular descriptors calculated from structure alone are used to describe the molecular structures. A subset of the calculated descriptors, selected using forward stepwise regression, is used in the QSPR models development. Multiple linear regression (MLR)and radial basis function neural networks (RBFNNs) are urilized to construct the linear and non-linear correlation model,respectively. The optimal QSPR model developedis based on a 7-17-1 RBFNNs architecture using seven calculated molecular descriptors. The root mean square errorsin predictions for the training, predicting and overall data sets are 0.284, 0.327 and 0.291 log P units, respectively.

  15. Radial Basis Function Neural Networks Based QSPR for the Prediction of log P

    Institute of Scientific and Technical Information of China (English)

    姚小军; 范波涛; 等

    2002-01-01

    Quantitative structure-property relationship(QSPR) method is used to study the correlation models between the structures of a set of diverse organic compounds and their log P.Molecular descriptors calculated from strucure alone are used to describe the molecular structures.A subset of the calcualted descriptors,selected using forward stepwise regression,is used in the QSPR models development.Multiple linear regression (MLR) and radial basis function neural networks (RBFNNs) are utilied to construct the linear and non-linear correlation model,respectively,The optimal QSPR model developed is based on a 7-17-1 RBFNNs architecture using sever calculated molecular descriptors .The root mean square errors in predictions for the training,predicting and overall data sets are 0.284,0.327 and 0.291 log P units respectively.

  16. Double hidden layer RBF process neural network based online prediction of steam turbine exhaust enthalpy

    Institute of Scientific and Technical Information of China (English)

    GONG Huanchun

    2014-01-01

    In order to diagnose the unit economic performance online,the radial basis function (RBF) process neural network with two hidden layers was introduced to online prediction of steam turbine exhaust enthalpy.Thus,the model reflecting complicated relationship between the steam turbine exhaust enthalpy and the relative operation parameters was established.Moreover,the enthalpy of final stage extraction steam and exhaust from a 300 MW unit turbine was taken as the example to perform the online calculation. The results show that,the average relative error of this method is less than 1%,so the accuracy of this al-gorithm is higher than that of the BP neutral network.Furthermore,this method has advantages of high convergence rate,simple structure and high accuracy.

  17. Novel stability criteria for fuzzy Hopfield neural networks based on an improved homogeneous matrix polynomials technique

    Institute of Scientific and Technical Information of China (English)

    Feng Yi-Fu; Zhang Qing-Ling; Feng De-Zhi

    2012-01-01

    The global stability problem of Takagi-Sugeno (T S) fuzzy Hopfield neural networks (FHNNs) with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism.Firstly,using both Finsler's lemma and an improved homogeneous matrix polynomial technique,and applying an affine parameter-dependent Lyapunov-Krasovskii functional,we obtain the convergent LMI-based stability criteria.Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique.Secondly,to further reduce the conservatism,a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs,which is suitable to the homogeneous matrix polynomials setting.Finally,two illustrative examples are given to show the efficiency of the proposed approaches.

  18. A Novel Neural Network Based Method Developed for Digit Recognition Applied to Automatic Speed Sign Recognition

    Directory of Open Access Journals (Sweden)

    Hanene Rouabeh

    2016-02-01

    Full Text Available This Paper presents a new hybrid technique for digit recognition applied to the speed limit sign recognition task. The complete recognition system consists in the detection and recognition of the speed signs in RGB images. A pretreatment is applied to extract the pictogram from a detected circular road sign, and then the task discussed in this work is employed to recognize digit candidates. To realize a compromise between performances, reduced execution time and optimized memory resources, the developed method is based on a conjoint use of a Neural Network and a Decision Tree. A simple Network is employed firstly to classify the extracted candidates into three classes and secondly a small Decision Tree is charged to determine the exact information. This combination is used to reduce the size of the Network as well as the memory resources utilization. The evaluation of the technique and the comparison with existent methods show the effectiveness.

  19. Neural Network Based Multi-level Fuzzy Evaluation Model for Mechanical Kinematic Scheme

    Institute of Scientific and Technical Information of China (English)

    BO Ruifeng; LI Ruiqin

    2006-01-01

    To implement a quantificational evaluation for mechanical kinematic scheme more effectively, a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly, the structure of evaluation model is constructed according to evaluation indicator system. Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result, as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model, the corresponding evaluation result is outputted and the best alternative can be selected. Under this model, expert knowledge can be effectively acquired and expressed, and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system. Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.

  20. Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification.

    Science.gov (United States)

    Sarkar, Sankho Turjo; Bhondekar, Amol P; Macaš, Martin; Kumar, Ritesh; Kaur, Rishemjit; Sharma, Anupma; Gulati, Ashu; Kumar, Amod

    2015-11-01

    The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis.

  1. A fast and scalable recurrent neural network based on stochastic meta descent.

    Science.gov (United States)

    Liu, Zhenzhen; Elhanany, Itamar

    2008-09-01

    This brief presents an efficient and scalable online learning algorithm for recurrent neural networks (RNNs). The approach is based on the real-time recurrent learning (RTRL) algorithm, whereby the sensitivity set of each neuron is reduced to weights associated with either its input or output links. This yields a reduced storage and computational complexity of O(N(2)). Stochastic meta descent (SMD), an adaptive step size scheme for stochastic gradient-descent problems, is employed as means of incorporating curvature information in order to substantially accelerate the learning process. We also introduce a clustered version of our algorithm to further improve its scalability attributes. Despite the dramatic reduction in resource requirements, it is shown through simulation results that the approach outperforms regular RTRL by almost an order of magnitude. Moreover, the scheme lends itself to parallel hardware realization by virtue of the localized property that is inherent to the learning framework. PMID:18779096

  2. Novel stability criteria for fuzzy Hopfield neural networks based on an improved homogeneous matrix polynomials technique

    International Nuclear Information System (INIS)

    The global stability problem of Takagi—Sugeno (T—S) fuzzy Hopfield neural networks (FHNNs) with time delays is investigated. Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism. Firstly, using both Finsler's lemma and an improved homogeneous matrix polynomial technique, and applying an affine parameter-dependent Lyapunov—Krasovskii functional, we obtain the convergent LMI-based stability criteria. Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique. Secondly, to further reduce the conservatism, a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs, which is suitable to the homogeneous matrix polynomials setting. Finally, two illustrative examples are given to show the efficiency of the proposed approaches

  3. Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete

    Directory of Open Access Journals (Sweden)

    Sangyong Kim

    2013-01-01

    Full Text Available This research aims to optimize the mixing proportion of recycled aggregate concrete (RAC using neural networks (NNs based on genetic algorithms (GAs for increasing the use of recycled aggregate (RA. NN and GA were used to predict the compressive strength of the concrete at 28 days. And sensitivity analysis of the NN based on GA was used to find the mixing ratio of RAC. The mixing criteria for RAC were determined and the replacement ratio of RAs was identified. This research reveal that the proposed method, which is NN based on GA, is proper for optimizing appropriate mixing proportion of RAC. Also, this method would help the construction engineers to utilize the recycled aggregate and reduce the concrete waste in construction process.

  4. Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection

    Directory of Open Access Journals (Sweden)

    Kang Xie

    2015-01-01

    Full Text Available According to the problems of current distributed architecture intrusion detection systems (DIDS, a new online distributed intrusion detection model based on cellular neural network (CNN was proposed, in which discrete-time CNN (DTCNN was used as weak classifier in each local node and state-controlled CNN (SCCNN was used as global detection method, respectively. We further proposed a new method for design template parameters of SCCNN via solving Linear Matrix Inequality. Experimental results based on KDD CUP 99 dataset show its feasibility and effectiveness. Emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI implementation which allows the distributed intrusion detection to be performed better.

  5. Neural network based cluster creation in the ATLAS silicon Pixel Detector

    CERN Document Server

    Perez Cavalcanti, T; The ATLAS collaboration

    2012-01-01

    The hit signals read out from pixels on planar semi-conductor sensors are grouped into clusters, to reconstruct the location where a charged particle passed through. The resolution of the individual pixel sizes can be improved significantly using the information from the cluster of adjacent pixels. Such analog cluster creation techniques have been used by the ATLAS experiment for many years giving an excellent performance. However, in dense environments, such as those inside high-energy jets, is likely that the charge deposited by two or more close-by tracks merges into one single cluster. A new pattern recognition algorithm based on neural network methods has been developed for the ATLAS Pixel Detector. This can identify the shared clusters, split them if necessary, and estimate the positions of all particles traversing the cluster. The algorithm significantly reduces ambiguities in the assignment of pixel detector measurements to tracks within jets, and improves the positional accuracy with respect to stand...

  6. Neural Network Based Reconstruction of a 3D Object from a 2D Wireframe

    CERN Document Server

    Johnson, Kyle; Lipson, Hod

    2010-01-01

    We propose a new approach for constructing a 3D representation from a 2D wireframe drawing. A drawing is simply a parallel projection of a 3D object onto a 2D surface; humans are able to recreate mental 3D models from 2D representations very easily, yet the process is very difficult to emulate computationally. We hypothesize that our ability to perform this construction relies on the angles in the 2D scene, among other geometric properties. Being able to reproduce this reconstruction process automatically would allow for efficient and robust 3D sketch interfaces. Our research focuses on the relationship between 2D geometry observable in the sketch and 3D geometry derived from a potential 3D construction. We present a fully automated system that constructs 3D representations from 2D wireframes using a neural network in conjunction with a genetic search algorithm.

  7. A Neural-Network-Based Semi-Automated Geospatial Classification Tool

    Science.gov (United States)

    Hale, R. G.; Herzfeld, U. C.

    2014-12-01

    North America's largest glacier system, the Bering Bagley Glacier System (BBGS) in Alaska, surged in 2011-2013, as shown by rapid mass transfer, elevation change, and heavy crevassing. Little is known about the physics controlling surge glaciers' semi-cyclic patterns; therefore, it is crucial to collect and analyze as much data as possible so that predictive models can be made. In addition, physical signs frozen in ice in the form of crevasses may help serve as a warning for future surges. The BBGS surge provided an opportunity to develop an automated classification tool for crevasse classification based on imagery collected from small aircraft. The classification allows one to link image classification to geophysical processes associated with ice deformation. The tool uses an approach that employs geostatistical functions and a feed-forward perceptron with error back-propagation. The connectionist-geostatistical approach uses directional experimental (discrete) variograms to parameterize images into a form that the Neural Network (NN) can recognize. In an application to preform analysis on airborne video graphic data from the surge of the BBGS, an NN was able to distinguish 18 different crevasse classes with 95 percent or higher accuracy, for over 3,000 images. Recognizing that each surge wave results in different crevasse types and that environmental conditions affect the appearance in imagery, we designed the tool's semi-automated pre-training algorithm to be adaptable. The tool can be optimized to specific settings and variables of image analysis: (airborne and satellite imagery, different camera types, observation altitude, number and types of classes, and resolution). The generalization of the classification tool brings three important advantages: (1) multiple types of problems in geophysics can be studied, (2) the training process is sufficiently formalized to allow non-experts in neural nets to perform the training process, and (3) the time required to

  8. A Neural Network based Path Planning Algorithm for Extinguishing Forest Fires

    Directory of Open Access Journals (Sweden)

    M.P.Sivaram Kumar

    2012-03-01

    Full Text Available In this work an algorithm for automatic detection and suppression of Forest fires is proposed. The algorithm is implemented using parallel distributed model of neural network with three activation functions to determine the next consecutive moves to the cells for the actor. The algorithm uses reinforcement learning with weights determined dynamically in each iteration. The Entire forest is decomposed into grid of square cells with initial position of the Actor is assumed to be the cell 1 and the goal cell is the cell where the fire has occurred. The neural network model uses starting cell, goal cell and number of cells in each row or column and three activation functions to determine the next consecutive cells in which the robot has to travel. It uses only three movements LEFT, DIAGONAL and UP to reach the target cell. After calculating next cell, the check will be made for presence of obstacles in that cell. If there is any obstacle in that cell, then one cell from other two cells obtained using other two movements, which is free from obstacle will be chosen for next move. Then the cell number is stored in memory. This process is repeated till the next cell computed is same as the goal cell. The Actor will begin to move from start cell and reach the goal cell using the cell numbers available in the memory to extinguish Forest fire. This algorithm is designed keeping in mind only static obstacles and hence it works well for Forest environment with static obstacles. Computer simulation results show that path has been found successfully without collision with obstacles.

  9. Prediction of monthly mean daily global solar radiation using Artificial Neural Network

    Indian Academy of Sciences (India)

    V Sivamadhavi; R Samuel Selvaraj

    2012-12-01

    In this study, a multilayer feed forward (MLFF) neural network based on back propagation algorithm was developed, trained, and tested to predict monthly mean daily global radiation in Tamil Nadu, India. Various geographical, solar and meteorological parameters of three different locations with diverse climatic conditions were used as input parameters. Out of 565 available data, 530 were used for training and the rest were used for testing the artificial neural network (ANN). A 3-layer and a 4-layer MLFF networks were developed and the performance of the developed models was evaluated based on mean bias error, mean absolute percentage error, root mean squared error and Student’s -test. The 3-layer MLFF network developed in this study did not give uniform results for the three chosen locations. Hence, a 4-layer MLFF network was developed and the average value of the mean absolute percentage error was found to be 5.47%. Values of global radiation obtained using the model were in excellent agreement with measured values. Results of this study show that the designed ANN model can be used to estimate monthly mean daily global radiation of any place in Tamil Nadu where measured global radiation data are not available.

  10. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

    2006-07-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  11. Landslide susceptibility analysis using an artificial neural network model

    Science.gov (United States)

    Mansor, Shattri; Pradhan, Biswajeet; Daud, Mohamed; Jamaludin, Normalina; Khuzaimah, Zailani

    2007-10-01

    This paper deals with landslide susceptibility analysis using an artificial neural network model for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide hazard was analyzed using landslide occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide susceptibility analysis was carried out using an artificial neural network model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.

  12. Prediction of Short Term Electric Load Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Jayant D. Sawarkar

    2012-06-01

    Full Text Available Artificial Neural Network (ANN Method is applied to fore cast the short-term load for a large power system. A nonlinear load model is proposed and several structures of ANN for short term forecasting are tested. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers is tested with various combinations of neurons, and the results are compared in terms of forecasting error. To demonstrate the effectiveness of the proposed approach, publicly available data from the State load Dispatch Centre, Airoli, Navi Mumbai’s web site has been taken to forecast the hourly load. We predicted the hourly load demand with a high degree of accuracy. Historical load data was divided into two parts where half of them are used for training and the other half is used for testing the ANN. Learning methods such as artificial neural networks, and more recently, support vector regression machines have been introduced to this field. In practices we often expect a fast forecasting, while standard algorithms based on the whole data set are time consuming. An absolute mean error of 2.26% was achieved when the trained network was tested on data

  13. Stellar Image Interpretation System Using Artificial Neural Networks:

    Directory of Open Access Journals (Sweden)

    A. El-Bassuny Alawy

    2004-01-01

    Full Text Available A supervised Artificial Neural Network (ANN based system is being developed employing the Bi-polar function for identifying stellar images in CCD frames. It is based on feed-forward artificial neural networks with error back-propagation learning. It has been coded in C language. The learning process was performed on a 341 input pattern set, while a similar set was used for testing. The present approach has been applied on a CCD frame of the open star cluster M67. The results obtained have been discussed and compared with those derived in our previous work employing the Uni-polar function and by a package known in the astronomical community (DAOPHOT-II. Full agreement was found between the present approach, that of Elnagahy et al, and the standard astronomical data for the cluster. It has been shown that the developed technique resembles that of the Uni-Polar function, possessing a simple, much faster yet reliable approach. Moreover, neither prior knowledge on, nor initial data from, the frame to be analysed is required, as it is for DAOPHOT-II. 

  14. Prediction aluminum corrosion inhibitor efficiency using artificial neural network (ANN)

    Science.gov (United States)

    Ebrahimi, Sh; Kalhor, E. G.; Nabavi, S. R.; Alamiparvin, L.; Pogaku, R.

    2016-06-01

    In this study, activity of some Schiff bases as aluminum corrosion inhibitor was investigated using artificial neural network (ANN). Hence, corrosion inhibition efficiency of Schiff bases (in any type) were gathered from different references. Then these molecules were drawn and optimized in Hyperchem software. Molecular descriptors generating and descriptors selection were fulfilled by Dragon software and principal component analysis (PCA) method, respectively. These structural descriptors along with environmental descriptors (ambient temperature, time of exposure, pH and the concentration of inhibitor) were used as input variables. Furthermore, aluminum corrosion inhibition efficiency was used as output variable. Experimental data were split into three sets: training set (for model building) and test set (for model validation) and simulation (for general model). Modeling was performed by Multiple linear regression (MLR) methods and artificial neural network (ANN). The results obtained in linear models showed poor correlation between experimental and theoretical data. However nonlinear model presented satisfactory results. Higher correlation coefficient of ANN (R > 0.9) revealed that ANN can be successfully applied for prediction of aluminum corrosion inhibitor efficiency of Schiff bases in different environmental conditions.

  15. Gap Filling of Daily Sea Levels by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Lyubka Pashova

    2013-06-01

    Full Text Available In the recent years, intelligent methods as artificial neural networks are successfully applied for data analysis from different fields of the geosciences. One of the encountered practical problems is the availability of gaps in the time series that prevent their comprehensive usage for the scientific and practical purposes. The article briefly describes two types of the artificial neural network (ANN architectures - Feed-Forward Backpropagation (FFBP and recurrent Echo state network (ESN. In some cases, the ANN can be used as an alternative on the traditional methods, to fill in missing values in the time series. We have been conducted several experiments to fill the missing values of daily sea levels spanning a 5-years period using both ANN architectures. A multiple linear regression for the same purpose has been also applied. The sea level data are derived from the records of the tide gauge Burgas, which is located on the western Black Sea coast. The achieved results have shown that the performance of ANN models is better than that of the classical one and they are very promising for the real-time interpolation of missing data in the time series.

  16. Application of Artificial Neural Network Approach for Estimating Reference Evapotranspiration

    Directory of Open Access Journals (Sweden)

    Khyati N. Vyas

    2016-08-01

    Full Text Available The process of evapotranspiration (ET is a vital part of the water cycle. Exact estimation of the value of ET is necessary for designing irrigation systems and water resources management. Accurate estimation of ET is essential in agriculture, its over-estimation leads to cause the waste of valuable water resources and its underestimation leads to the plant moisture stress and decrease in the crop yield. The well known Penman-Monteith (PM equation always performs the highest accuracy results of estimating reference Evapotranspiration (ET0 among the existing methods is without any discussion. However, the equation requires climatic data that are not always available particularly for a developing country. ET0 is a complex process which is depending on a number of interacting meteorological factors, such as temperature, humidity, wind speed, and radiation. The lack of physical understanding of ET0 process and unavailability of all appropriate data results in imprecise estimation of ET0. Over the past two decades, artificial neural networks (ANNs have been increasingly applied in modeling of hydrological processes because of their ability in mapping the input–output relationship without any understanding of physical process. This paper investigates for the first time in the semiarid environment of Junagadh, the potential of an artificial neural network (ANN for estimating ET0 with limited climatic data set.

  17. Predicting oil price movements: A dynamic Artificial Neural Network approach

    International Nuclear Information System (INIS)

    Price of oil is important for the economies of oil exporting and oil importing countries alike. Therefore, insight into the likely future behaviour and patterns of oil prices can improve economic planning and reduce the impacts of oil market fluctuations. This paper aims to improve the application of Artificial Neural Network (ANN) techniques to prediction of oil price. We develop a dynamic Nonlinear Auto Regressive model with eXogenous input (NARX) as a form of ANN to account for the time factor. We estimate the model using macroeconomic data from OECD countries. In order to compare the results, we develop time series and ANN static models. We then use the output of time series model to develop a NARX model. The NARX model is trained with historical data from 1974 to 2004 and the results are verified with data from 2005 to 2009. The results show that NARX model is more accurate than time series and static ANN models in predicting oil prices in general as well as in predicting the occurrence of oil price shocks. - Highlights: • Nonlinear Auto Regressive model with eXogenous (NARX) inputs is developed for predicting oil prices. • The results of NARX model in oil price forecasting is more accurate than those of time series and Artificial Neural Network. • The NARX model predicts the price shocks in the oil market. • The NARX model is dynamic and accounts for the factor of time

  18. Neural network based approach to study the effect of feature selection on document summarization

    Directory of Open Access Journals (Sweden)

    Dipti Y. Sakhare

    2013-06-01

    Full Text Available As the amount of textual Information increases, we experience a need for Automatic Text Summarizers. In Automatic summarization a text document or a larger corpus of multiple documents are reduced to a short set of words or paragraph that conveys the main meaning of the text. In this paper we proposed various features of Summary extraction. In the proposed approach during training phase, the feature vector is computed for a set of sentences using the feature extraction technique. After that, the feature vector and their corresponding feature scores are used to train the neural network optimally. Later in the testing phase, the input document is subjectedto pre-processing and feature extraction techniques. Finally, by making use of sentence score, the most important sentences are extracted from the input document. The experimentation is performed with the DUC 2002 dataset. The features that are to be applied depending upon the size of the Document are also analyzed. The comparative results of the proposed approach and that of MS-Word are also presented here.

  19. Short-term load and wind power forecasting using neural network-based prediction intervals.

    Science.gov (United States)

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2014-02-01

    Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

  20. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

    Directory of Open Access Journals (Sweden)

    Srdjan Sladojevic

    2016-01-01

    Full Text Available The latest generation of convolutional neural networks (CNNs has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

  1. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

    Science.gov (United States)

    Sladojevic, Srdjan; Arsenovic, Marko; Culibrk, Dubravko; Stefanovic, Darko

    2016-01-01

    The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%. PMID:27418923

  2. Discriminative boosted forest with convolutional neural network-based patch descriptor for object detection

    Science.gov (United States)

    Xiang, Tao; Li, Tao; Ye, Mao; Li, Xudong

    2016-01-01

    Object detection with intraclass variations is challenging. The existing methods have not achieved the optimal combinations of classifiers and features, especially features learned by convolutional neural networks (CNNs). To solve this problem, we propose an object-detection method based on improved random forest and local image patches represented by CNN features. First, we compute CNN-based patch descriptors for each sample by modified CNNs. Then, the random forest is built whose split functions are defined by patch selector and linear projection learned by linear support vector machine. To improve the classification accuracy, the split functions in each depth of the forest make up a local classifier, and all local classifiers are assembled in a layer-wise manner by a boosting algorithm. The main contributions of our approach are summarized as follows: (1) We propose a new local patch descriptor based on CNN features. (2) We define a patch-based split function which is optimized with maximum class-label purity and minimum classification error over the samples of the node. (3) Each local classifier is assembled by minimizing the global classification error. We evaluate the method on three well-known challenging datasets: TUD pedestrians, INRIA pedestrians, and UIUC cars. The experiments demonstrate that our method achieves state-of-the-art or competitive performance.

  3. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification.

    Science.gov (United States)

    Sladojevic, Srdjan; Arsenovic, Marko; Anderla, Andras; Culibrk, Dubravko; Stefanovic, Darko

    2016-01-01

    The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%. PMID:27418923

  4. Classification of Cancer Gene Selection Using Random Forest and Neural Network Based Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Jogendra Kushwah

    2013-06-01

    Full Text Available The free radical gene classification of cancerdiseasesis challenging job in biomedical dataengineering. The improving of classification of geneselection of cancer diseases various classifier areused, but the classification of classifier are notvalidate. So ensemble classifier is used for cancergene classification using neural network classifierwith random forest tree. The random forest tree isensembling technique of classifier in this techniquethe number of classifier ensemble of their leaf nodeof class of classifier. In this paper we combinedneuralnetwork with random forest ensembleclassifier for classification of cancer gene selectionfor diagnose analysis of cancer diseases.Theproposed method is different from most of themethods of ensemble classifier, which follow aninput output paradigm ofneural network, where themembers of the ensemble are selected from a set ofneural network classifier. the number of classifiersis determined during the rising procedure of theforest. Furthermore, the proposed method producesan ensemble not only correct, but also assorted,ensuring the two important properties that shouldcharacterize an ensemble classifier. For empiricalevaluation of our proposed method we used UCIcancer diseases data set for classification. Ourexperimental result shows that betterresult incompression of random forest tree classification

  5. Neural network based approach for time to crash prediction to cope with software aging

    Institute of Scientific and Technical Information of China (English)

    Moona Yakhchi; Javier Alonso; Mahdi Fazeli; Amir Akhavan Bitaraf; Ahmad Patooghy

    2015-01-01

    Recent studies have shown that software is one of the main reasons for computer systems unavailability. A growing ac-cumulation of software errors with time causes a phenomenon cal ed software aging. This phenomenon can result in system per-formance degradation and eventual y system hang/crash. To cope with software aging, software rejuvenation has been proposed. Software rejuvenation is a proactive technique which leads to re-moving the accumulated software errors by stopping the system, cleaning up its internal state, and resuming its normal operation. One of the main chal enges of software rejuvenation is accurately predicting the time to crash due to aging factors such as me-mory leaks. In this paper, different machine learning techniques are compared to accurately predict the software time to crash un-der different aging scenarios. Final y, by comparing the accuracy of different techniques, it can be concluded that the multilayer per-ceptron neural network has the highest prediction accuracy among al techniques studied.

  6. Neural-network-based adaptive UPFC for improving transient stability performance of power system.

    Science.gov (United States)

    Mishra, Sukumar

    2006-03-01

    This paper uses the recently proposed H(infinity)-learning method, for updating the parameter of the radial basis function neural network (RBFNN) used as a control scheme for the unified power flow controller (UPFC) to improve the transient stability performance of a multimachine power system. The RBFNN uses a single neuron architecture whose input is proportional to the difference in error and the updating of its parameters is carried via a proportional value of the error. Also, the coefficients of the difference of error, error, and auxiliary signal used for improving damping performance are depicted by a genetic algorithm. The performance of the newly designed controller is evaluated in a four-machine power system subjected to different types of disturbances. The newly designed single-neuron RBFNN-based UPFC exhibits better damping performance compared to the conventional PID as well as the extended Kalman filter (EKF) updating-based RBFNN scheme, making the unstable cases stable. Its simple architecture reduces the computational burden, thereby making it attractive for real-time implementation. Also, all the machines are being equipped with the conventional power system stabilizer (PSS) to study the coordinated effect of UPFC and PSS in the system. PMID:16566472

  7. Gesture Recognition Using Neural Networks Based on HW/SW Cosimulation Platform

    Directory of Open Access Journals (Sweden)

    Priyanka Mekala

    2013-01-01

    Full Text Available Hardware/software (HW/SW cosimulation integrates software simulation and hardware simulation simultaneously. Usually, HW/SW co-simulation platform is used to ease debugging and verification for very large-scale integration (VLSI design. To accelerate the computation of the gesture recognition technique, an HW/SW implementation using field programmable gate array (FPGA technology is presented in this paper. The major contributions of this work are: (1 a novel design of memory controller in the Verilog Hardware Description Language (Verilog HDL to reduce memory consumption and load on the processor. (2 The testing part of the neural network algorithm is being hardwired to improve the speed and performance. The American Sign Language gesture recognition is chosen to verify the performance of the approach. Several experiments were carried out on four databases of the gestures (alphabet signs A to Z. (3 The major benefit of this design is that it takes only few milliseconds to recognize the hand gesture which makes it computationally more efficient.

  8. Neural network-based finite horizon stochastic optimal control design for nonlinear networked control systems.

    Science.gov (United States)

    Xu, Hao; Jagannathan, Sarangapani

    2015-03-01

    The stochastic optimal control of nonlinear networked control systems (NNCSs) using neuro-dynamic programming (NDP) over a finite time horizon is a challenging problem due to terminal constraints, system uncertainties, and unknown network imperfections, such as network-induced delays and packet losses. Since the traditional iteration or time-based infinite horizon NDP schemes are unsuitable for NNCS with terminal constraints, a novel time-based NDP scheme is developed to solve finite horizon optimal control of NNCS by mitigating the above-mentioned challenges. First, an online neural network (NN) identifier is introduced to approximate the control coefficient matrix that is subsequently utilized in conjunction with the critic and actor NNs to determine a time-based stochastic optimal control input over finite horizon in a forward-in-time and online manner. Eventually, Lyapunov theory is used to show that all closed-loop signals and NN weights are uniformly ultimately bounded with ultimate bounds being a function of initial conditions and final time. Moreover, the approximated control input converges close to optimal value within finite time. The simulation results are included to show the effectiveness of the proposed scheme. PMID:25720004

  9. A neural-network-based exponential H∞ synchronisation for chaotic secure communication via improved genetic algorithm

    Science.gov (United States)

    Hsiao, Feng-Hsiag

    2016-10-01

    In this study, a novel approach via improved genetic algorithm (IGA)-based fuzzy observer is proposed to realise exponential optimal H∞ synchronisation and secure communication in multiple time-delay chaotic (MTDC) systems. First, an original message is inserted into the MTDC system. Then, a neural-network (NN) model is employed to approximate the MTDC system. Next, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. Based on this LDI state-space representation, this study proposes a delay-dependent exponential stability criterion derived in terms of Lyapunov's direct method, thus ensuring that the trajectories of the slave system approach those of the master system. Subsequently, the stability condition of this criterion is reformulated into a linear matrix inequality (LMI). Due to GA's random global optimisation search capabilities, the lower and upper bounds of the search space can be set so that the GA will seek better fuzzy observer feedback gains, accelerating feedback gain-based synchronisation via the LMI-based approach. IGA, which exhibits better performance than traditional GA, is used to synthesise a fuzzy observer to not only realise the exponential synchronisation, but also achieve optimal H∞ performance by minimizing the disturbance attenuation level and recovering the transmitted message. Finally, a numerical example with simulations is given in order to demonstrate the effectiveness of our approach.

  10. Neural networks based three-axis satellite attitude control using only magnetic torquers

    International Nuclear Information System (INIS)

    Full text: Magnetic control is a favorable way to stabilize small satellites. Often, the hardware is simple and lightweight, and does not degrade or change mass over time. However, a magnetic control system does have some disadvantages and limitations. The control, which is in the form of magnetic moment, can only be applied perpendicular to the local magnetic field. In addition, there is uncertainty in the Earth magnetic field models due to the complicated dynamic nature of the field. Also, the magnetic hardware and the spacecraft can interact, causing both to behave in undesirable ways. To overcome these limitations some intelligence is incorporated in the controller. In this paper, control laws are developed to stabilize spacecraft on Three axes. The motivation for this project is ANUSAT, which is a micro-satellite under development at Anna University in collaboration with ISRO. This control could be carried out solely with satellite's magnetometer measurements and its position in orbit. The magnetic dipole moment for control is: M = Kp (Bo - Br) + Kd (dBo/dt - dBr/dt) Where Bo is the measured magnetic field, Br is the reference magnetic field, and Kp and Kd are the control position and rate gains respectively. The value of the controller gains are selected by the Intelligent Neural Network System in the feedback path. Control laws are numerically tested to show that the magnetic control system works within resolution limits

  11. A Proposal for Energy-Efficient Cellular Neural Network Based on Spintronic Devices

    Science.gov (United States)

    Pan, Chenyun; Naeemi, Azad

    2016-09-01

    Due to the massive parallel computing capability and outstanding image and signal processing performance, cellular neural network (CNN) is one promising type of non-Boolean computing system that can outperform the traditional digital logic computation and mitigate the physical scaling limit of the conventional CMOS technology. The CNN was originally implemented by VLSI analog technologies with operational amplifiers and operational transconductance amplifiers as neurons and synapses, respectively, which are power and area consuming. In this paper, we propose a hybrid structure to implement the CNN with magnetic components and CMOS peripherals with a complete driving and sensing circuitry. In addition, we propose a digitally programmable magnetic synapse that can achieve both positive and negative values of the templates. After rigorous performance analyses and comparisons, optimal energy is achieved based on various design parameters, including the driving voltage and the CMOS driving size. At a comparable footprint area and operation speed, a spintronic CNN is projected to achieve more than one order of magnitude energy reduction per operation compared to its CMOS counterpart.

  12. A Real Valued Neural Network Based Autoregressive Energy Detector for Cognitive Radio Application.

    Science.gov (United States)

    Onumanyi, A J; Onwuka, E N; Aibinu, A M; Ugweje, O C; Salami, M J E

    2014-01-01

    A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application.

  13. An Artificial Neural Networks-Based on-Line Monitoring Odor Sensing System

    OpenAIRE

    Yousif A. Bastaki

    2009-01-01

    Problem statement: There have been many works for odor recognition using different sensor arrays and pattern recognition techniques in last decades. Approach: Although an odor is usually recorded utilizing language expression, it is too difficult for laymen to associate actual odor with that expression. Results: The odor sensing system should be extended to new areas since its standard style where the output pattern from multiple sensors with partially overlapped specificity is recognized by ...

  14. Investigation of Artificial Neural Network Based Direct Torque Control for PMSM by Numerical Simulations

    Directory of Open Access Journals (Sweden)

    Fatih Korkmaz

    2012-12-01

    Full Text Available The International Journal of Computer Networks & Communications (IJCNC is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications.The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.

  15. A neural network-based optimal spatial filter design method for motor imagery classification.

    Directory of Open Access Journals (Sweden)

    Ayhan Yuksel

    Full Text Available In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy.

  16. A Neural-Network-Based Nonlinear Adaptive State-Observer for Pressurized Water Reactors

    Directory of Open Access Journals (Sweden)

    Zhe Dong

    2013-10-01

    Full Text Available Although there have been some severe nuclear accidents such as Three Mile Island (USA, Chernobyl (Ukraine and Fukushima (Japan, nuclear fission energy is still a source of clean energy that can substitute for fossil fuels in a centralized way and in a great amount with commercial availability and economic competitiveness. Since the pressurized water reactor (PWR is the most widely used nuclear fission reactor, its safe, stable and efficient operation is meaningful to the current rebirth of the nuclear fission energy industry. Power-level regulation is an important technique which can deeply affect the operation stability and efficiency of PWRs. Compared with the classical power-level controllers, the advanced power-level regulators could strengthen both the closed-loop stability and control performance by feeding back the internal state-variables. However, not all of the internal state variables of a PWR can be obtained directly by measurements. To implement advanced PWR power-level control law, it is necessary to develop a state-observer to reconstruct the unmeasurable state-variables. Since a PWR is naturally a complex nonlinear system with parameters varying with power-level, fuel burnup, xenon isotope production, control rod worth and etc., it is meaningful to design a nonlinear observer for the PWR with adaptability to system uncertainties. Due to this and the strong learning capability of the multi-layer perceptron (MLP neural network, an MLP-based nonlinear adaptive observer is given for PWRs. Based upon Lyapunov stability theory, it is proved theoretically that this newly-built observer can provide bounded and convergent state-observation. This observer is then applied to the state-observation of a special PWR, i.e., the nuclear heating reactor (NHR, and numerical simulation results not only verify its feasibility but also give the relationship between the observation performance and observer parameters.

  17. Neural network-based recognition of whistlers on spectrograms detected by satellite

    Science.gov (United States)

    Conti, Livio

    2016-04-01

    We present a system to automatically recognize and classify the occurrence of whistler waves on spectrograms of electric field measurements performed by satellite. Whistlers - VLF waves generated by lightning, with a specific spectral dispersion relation - can induce precipitation of trapped Van Allen particles and have a role in the chemistry of some atmospheric components (mainly NOx). Moreover, it has also been suggested that the increase of the number of anomalous whistlers (i.e. whistlers with high value of dispersion constant) could be induced by disturbances in the Earth-ionosphere wave-guide, generated by seismo-electromagnetic emissions. On satellite, the recognition of whistlers asks for analyzing high-resolution spectrograms that cannot be downloaded to Earth, due to the limits of data transmission. For this reason, a real time identification and classification must be performed on satellite, by avoiding downloading all the unprocessed data. The procedure that we have developed is based on a Time Delay Neural Network (TDNN). The TDNN, proposed some years ago for speech recognition, can be fruitfully also applied in real-time analysis of electromagnetic spectrograms in order to detect phenomena characterized by a specific shape/signature such as those of the whistler waves. Some studies have been performed by the RNF experiment on board of the DEMETER satellite and our algorithm could be adopted on board of the satellite CSES (China Seismo-Electromagnetic Satellite), launch scheduled by the end of 2016. Moreover, the procedure can be also adopted to automatic analysis of whistlers detected on ground.

  18. Neural Network-Based Self-Tuning PID Control for Underwater Vehicles.

    Science.gov (United States)

    Hernández-Alvarado, Rodrigo; García-Valdovinos, Luis Govinda; Salgado-Jiménez, Tomás; Gómez-Espinosa, Alfonso; Fonseca-Navarro, Fernando

    2016-01-01

    For decades, PID (Proportional + Integral + Derivative)-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme. PMID:27608018

  19. Integrated control of the cooling system and surface openings using the artificial neural networks

    International Nuclear Information System (INIS)

    This study aimed at suggesting an indoor temperature control method that can provide a comfortable thermal environment through the integrated control of the cooling system and the surface openings. Four control logic were developed, employing different application levels of rules and artificial neural network models. Rule-based control methods represented the conventional approach while ANN-based methods were applied for the predictive and adaptive controls. Comparative performance tests for the conventional- and ANN-based methods were numerically conducted for the double-skin-facade building, using the MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation) software, after proving the validity by comparing the simulation and field measurement results. Analysis revealed that the ANN-based controls of the cooling system and surface openings improved the indoor temperature conditions with increased comfortable temperature periods and decreased standard deviation of the indoor temperature from the center of the comfortable range. In addition, the proposed ANN-based logic effectively reduced the number of operating condition changes of the cooling system and surface openings, which can prevent system failure. The ANN-based logic, however, did not show superiority in energy efficiency over the conventional logic. Instead, they have increased the amount of heat removal by the cooling system. From the analysis, it can be concluded that the ANN-based temperature control logic was able to keep the indoor temperature more comfortably and stably within the comfortable range due to its predictive and adaptive features. - Highlights: • Integrated rule-based and artificial neural network based logics were developed. • A cooling device and surface openings were controlled in an integrated manner. • Computer simulation method was employed for comparative performance tests. • ANN-based logics showed the advanced features of thermal environment. • Rule

  20. Artificial Neural Networks for Diagnosis of Kidney Stones Disease

    Directory of Open Access Journals (Sweden)

    Koushal Kumar

    2012-07-01

    Full Text Available Artificial Neural networks are often used as a powerful discriminating classifier for tasks in medical diagnosis for early detection of diseases. They have several advantages over parametric classifiers such as discriminate analysis. The objective of this paper is to diagnose kidney stone disease by using three different neural network algorithms which have different architecture and characteristics. The aim of this work is to compare the performance of all three neural networks on the basis of its accuracy, time taken to build model, and training data set size. We will use Learning vector quantization (LVQ, two layers feed forward perceptron trained with back propagation training algorithm and Radial basis function (RBF networks for diagnosis of kidney stone disease. In this work we used Waikato Environment for Knowledge Analysis (WEKA version 3.7.5 as simulation tool which is an open source tool. The data set we used for diagnosis is real world data with 1000 instances and 8 attributes. In the end part we check the performance comparison of different algorithms to propose the best algorithm for kidney stone diagnosis. So this will helps in early identification of kidney stone in patients and reduces the diagnosis time.

  1. Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models

    Directory of Open Access Journals (Sweden)

    Christopher Gan

    2005-01-01

    Full Text Available Conventional econometric models, such as discriminant analysis and logistic regression have been used to predict consumer choice. However, in recent years, there has been a growing interest in applying artificial neural networks (ANN to analyse consumer behaviour and to model the consumer decision-making process. The purpose of this paper is to empirically compare the predictive power of the probability neural network (PNN, a special class of neural networks and a MLFN with a logistic model on consumers’ choices between electronic banking and non-electronic banking. Data for this analysis was obtained through a mail survey sent to 1,960 New Zealand households. The questionnaire gathered information on the factors consumers’ use to decide between electronic banking versus non-electronic banking. The factors include service quality dimensions, perceived risk factors, user input factors, price factors, service product characteristics and individual factors. In addition, demographic variables including age, gender, marital status, ethnic background, educational qualification, employment, income and area of residence are considered in the analysis. Empirical results showed that both ANN models (MLFN and PNN exhibit a higher overall percentage correct on consumer choice predictions than the logistic model. Furthermore, the PNN demonstrates to be the best predictive model since it has the highest overall percentage correct and a very low percentage error on both Type I and Type II errors.

  2. Modeling of methane emissions using artificial neural network approach

    Directory of Open Access Journals (Sweden)

    Stamenković Lidija J.

    2015-01-01

    Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007

  3. The performance evaluation of a new neural network based traffic management scheme for a satellite communication network

    Science.gov (United States)

    Ansari, Nirwan; Liu, Dequan

    1991-01-01

    A neural-network-based traffic management scheme for a satellite communication network is described. The scheme consists of two levels of management. The front end of the scheme is a derivation of Kohonen's self-organization model to configure maps for the satellite communication network dynamically. The model consists of three stages. The first stage is the pattern recognition task, in which an exemplar map that best meets the current network requirements is selected. The second stage is the analysis of the discrepancy between the chosen exemplar map and the state of the network, and the adaptive modification of the chosen exemplar map to conform closely to the network requirement (input data pattern) by means of Kohonen's self-organization. On the basis of certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. A state-dependent routing algorithm, which arranges the incoming call to some proper path, is used to make the network more efficient and to lower the call block rate. Simulation results demonstrate that the scheme, which combines self-organization and the state-dependent routing mechanism, provides better performance in terms of call block rate than schemes that only have either the self-organization mechanism or the routing mechanism.

  4. Neural network based control of an absorption column in the process of bioethanol production

    Directory of Open Access Journals (Sweden)

    Eduardo Eyng

    2009-08-01

    Full Text Available Gaseous ethanol may be recovered from the effluent gas mixture of the sugar cane fermentation process using a staged absorption column. In the present work, the development of a nonlinear controller, based on a neural network inverse model (ANN controller, was proposed and tested to manipulate the absorbent flow rate in order to control the residual ethanol concentration in the effluent gas phase. Simulation studies were carried out, in which a noise was applied to the ethanol concentration signals from the rigorous model. The ANN controller outperformed the dynamic matrix control (DMC when step disturbances were imposed to the gas mixture composition. A security device, based on a conventional feedback algorithm, and a digital filter were added to the proposed strategy to improve the system robustness when unforeseen operating and environmental conditions occured. The results demonstrated that ANN controller was a robust and reliable tool to control the absorption column.Deseja-se recuperar o etanol perdido por evaporação durante o processo de fermentação da cana-de-açúcar. Para tanto, faz-se uso de uma coluna de absorção. O controle da concentração de etanol no efluente gasoso da coluna é realizado pela manipulação da vazão de solvente, sendo esta determinada pelo controlador não linear proposto, baseado em um modelo inverso de redes neurais (controlador ANN. Foram feitas simulações adicionando-se um sinal de ruído a medida de concentração de etanol na fase gasosa. Quando perturbações degrau foram inseridas na mistura gasosa afluente, o controlador ANN demonstrou desempenho superior ao controle por matriz dinâmica (DMC. Um dispositivo de segurança, baseado em um controlador feedback convencional, e um filtro digital foram implementados à estratégia de controle proposta para agregar robustez no tratamento de distúrbios ocorridos no ambiente operacional. Os resultados demonstraram que o controlador ANN é uma

  5. Neutron spectrometry and dosimetry based on a new approach called Genetic Artificial Neural Networks

    International Nuclear Information System (INIS)

    Artificial Neural Networks and Genetic Algorithms are two relatively young research areas that were subject to a steadily growing interest during the past years. The structure of a neural network is a significant contributing factor to its performance and the structure is generally heuristically chosen. The use of evolutionary algorithms as search techniques has allowed different properties of neural networks to be evolved. This paper focuses on the intersection on neural networks and evolutionary computation, namely on how evolutionary algorithms can be used to assist neural network design and training, as a novel approach. In this research, a new evolvable artificial neural network modelling approach is presented, which utilizes an optimization process based on the combination of genetic algorithms and artificial neural networks, and is applied in the design of a neural network, oriented to solve the neutron spectrometry and simultaneous dosimetry problems, using only the count rates measured with a Bonner spheres spectrometer system as entrance data. (author)

  6. Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

    Science.gov (United States)

    Jafari, Mehdi; Kasaei, Shohreh

    2012-01-01

    Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

  7. Ascending Thermal Localization and Its Strongest Zone Centering by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ivan Suzdalev

    2011-04-01

    Full Text Available Thermal localization and their strongest zone centering by artificial neural networks (ANN, and it are used by the automatic or semiautomatic control system of unmanned aerial vehicles (UAV. Artificial neural network take input data from aircraft avionics. Actual thermal model of space and its value’s correlation with other factors are researched as well. Article in Lithuanian

  8. Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Høgsberg, Jan Becker; Winther, Ole

    2011-01-01

    It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is able...

  9. Artificial Neural Network L* from different magnetospheric field models

    Science.gov (United States)

    Yu, Y.; Koller, J.; Zaharia, S. G.; Jordanova, V. K.

    2011-12-01

    The third adiabatic invariant L* plays an important role in modeling and understanding the radiation belt dynamics. The popular way to numerically obtain the L* value follows the recipe described by Roederer [1970], which is, however, slow and computational expensive. This work focuses on a new technique, which can compute the L* value in microseconds without losing much accuracy: artificial neural networks. Since L* is related to the magnetic flux enclosed by a particle drift shell, global magnetic field information needed to trace the drift shell is required. A series of currently popular empirical magnetic field models are applied to create the L* data pool using 1 million data samples which are randomly selected within a solar cycle and within the global magnetosphere. The networks, trained from the above L* data pool, can thereby be used for fairly efficient L* calculation given input parameters valid within the trained temporal and spatial range. Besides the empirical magnetospheric models, a physics-based self-consistent inner magnetosphere model (RAM-SCB) developed at LANL is also utilized to calculate L* values and then to train the L* neural network. This model better predicts the magnetospheric configuration and therefore can significantly improve the L*. The above neural network L* technique will enable, for the first time, comprehensive solar-cycle long studies of radiation belt processes. However, neural networks trained from different magnetic field models can result in different L* values, which could cause mis-interpretation of radiation belt dynamics, such as where the source of the radiation belt charged particle is and which mechanism is dominant in accelerating the particles. Such a fact calls for attention to cautiously choose a magnetospheric field model for the L* calculation.

  10. Identifing Atmospheric Pollutant Sources Using Artificial Neural Networks

    Science.gov (United States)

    Paes, F. F.; Campos, H. F.; Luz, E. P.; Carvalho, A. R.

    2008-05-01

    The estimation of the area source pollutant strength is a relevant issue for atmospheric environment. This characterizes an inverse problem in the atmospheric pollution dispersion. In the inverse analysis, an area source domain is considered, where the strength of such area source term is assumed unknown. The inverse problem is solved by using a supervised artificial neural network: multi-layer perceptron. The conection weights of the neural network are computed from delta rule - learning process. The neural network inversion is compared with results from standard inverse analysis (regularized inverse solution). In the regularization method, the inverse problem is formulated as a non-linear optimization approach, whose the objective function is given by the square difference between the measured pollutant concentration and the mathematical models, associated with a regularization operator. In our numerical experiments, the forward problem is addressed by a source-receptor scheme, where a regressive Lagrangian model is applied to compute the transition matrix. The second order maximum entropy regularization is used, and the regularization parameter is calculated by the L-curve technique. The objective function is minimized employing a deterministic scheme (a quasi-Newton algorithm) [1] and a stochastic technique (PSO: particle swarm optimization) [2]. The inverse problem methodology is tested with synthetic observational data, from six measurement points in the physical domain. The best inverse solutions were obtained with neural networks. References: [1] D. R. Roberti, D. Anfossi, H. F. Campos Velho, G. A. Degrazia (2005): Estimating Emission Rate and Pollutant Source Location, Ciencia e Natura, p. 131-134. [2] E.F.P. da Luz, H.F. de Campos Velho, J.C. Becceneri, D.R. Roberti (2007): Estimating Atmospheric Area Source Strength Through Particle Swarm Optimization. Inverse Problems, Desing and Optimization Symposium IPDO-2007, April 16-18, Miami (FL), USA, vol 1, p

  11. Precursors predicted by artificial neural networks for mass balance calculations: Quantifying hydrothermal alteration in volcanic rocks

    Science.gov (United States)

    Trépanier, Sylvain; Mathieu, Lucie; Daigneault, Réal; Faure, Stéphane

    2016-04-01

    This study proposes an artificial neural networks-based method for predicting the unaltered (precursor) chemical compositions of hydrothermally altered volcanic rock. The method aims at predicting precursor's major components contents (SiO2, FeOT, MgO, CaO, Na2O, and K2O). The prediction is based on ratios of elements generally immobile during alteration processes; i.e. Zr, TiO2, Al2O3, Y, Nb, Th, and Cr, which are provided as inputs to the neural networks. Multi-layer perceptron neural networks were trained on a large dataset of least-altered volcanic rock samples that document a wide range of volcanic rock types, tectonic settings and ages. The precursors thus predicted are then used to perform mass balance calculations. Various statistics were calculated to validate the predictions of precursors' major components, which indicate that, overall, the predictions are precise and accurate. For example, rank-based correlation coefficients were calculated to compare predicted and analysed values from a least-altered test dataset that had not been used to train the networks. Coefficients over 0.87 were obtained for all components, except for Na2O (0.77), indicating that predictions for alkali might be less performant. Also, predictions are performant for most volcanic rock compositions, except for ultra-K rocks. The proposed method provides an easy and rapid solution to the often difficult task of determining appropriate volcanic precursor compositions to rocks modified by hydrothermal alteration. It is intended for large volcanic rock databases and is most useful, for example, to mineral exploration performed in complex or poorly known volcanic settings. The method is implemented as a simple C++ console program.

  12. Artificial Neural Network approach to develop unique Classification and Raga identification tools for Pattern Recognition in Carnatic Music

    Science.gov (United States)

    Srimani, P. K.; Parimala, Y. G.

    2011-12-01

    A unique approach has been developed to study patterns in ragas of Carnatic Classical music based on artificial neural networks. Ragas in Carnatic music which have found their roots in the Vedic period, have grown on a Scientific foundation over thousands of years. However owing to its vastness and complexities it has always been a challenge for scientists and musicologists to give an all encompassing perspective both qualitatively and quantitatively. Cognition, comprehension and perception of ragas in Indian classical music have always been the subject of intensive research, highly intriguing and many facets of these are hitherto not unravelled. This paper is an attempt to view the melakartha ragas with a cognitive perspective using artificial neural network based approach which has given raise to very interesting results. The 72 ragas of the melakartha system were defined through the combination of frequencies occurring in each of them. The data sets were trained using several neural networks. 100% accurate pattern recognition and classification was obtained using linear regression, TLRN, MLP and RBF networks. Performance of the different network topologies, by varying various network parameters, were compared. Linear regression was found to be the best performing network.

  13. Applications of artificial neural networks (ANNs) in food science.

    Science.gov (United States)

    Huang, Yiqun; Kangas, Lars J; Rasco, Barbara A

    2007-01-01

    Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decades, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs hold a great deal of promise for modeling complex tasks in process control and simulation and in applications of machine perception including machine vision and electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in the field.

  14. Selection in sugarcane families with artificial neural networks

    Directory of Open Access Journals (Sweden)

    Bruno Portela Brasileiro

    2015-04-01

    Full Text Available The objective of this study was to evaluate Artificial Neural Networks (ANN applied in an selection process within sugarcane families. The best ANN model produced no mistake, but was able to classify all genotypes correctly, i.e., the network made the same selective choice as the breeder during the simulation individual best linear unbiased predictor (BLUPIS, demonstrating the ability of the ANN to learn from the inputs and outputs provided in the training and validation phases. Since the ANN-based selection facilitates the identification of the best plants and the development of a new selection strategy in the best families, to ensure that the best genotypes of the population are evaluated in the following stages of the breeding program, we recommend to rank families by BLUP, followed by selection of the best families and finally, select the seedlings by ANN, from information at the individual level in the best families.

  15. Automatic segmentation of cerebral MR images using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Alirezaie, J.; Jernigan, M.E. [Univ. of Waterloo, Ontario (Canada); Nahmias, C. [McMaster Univ., Hamilton, Ontario (Canada)

    1996-12-31

    In this paper we present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Our scheme utilizes the Self Organizing Feature Map (SOFM) artificial neural network for feature mapping and generates a set of codebook vectors. By extending the network with an additional layer the map will be classified and each tissue class will be labelled. An algorithm has been developed for extracting the cerebrum from the head scan prior to the segmentation. Extracting the cerebrum is performed by stripping away the skull pixels from the T2 image. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. To compare the results with other conventional approaches we applied the c-means algorithm to the problem.

  16. Artificial neural network analysis of triple effect absorption refrigeration systems

    Energy Technology Data Exchange (ETDEWEB)

    Hajizadeh Aghdam, A. [Department of Mechanical Engineering, Islamic Azad University (Iran, Islamic Republic of)], email: a.hajizadeh@iaukashan.ac.ir; Nazmara, H.; Farzaneh, B. [Department of Mechanical Engineering, University of Tabriz (Iran, Islamic Republic of)], email: h.nazmara@nioec.org, email: b_farzaneh_ms@yahoo.com

    2011-07-01

    In this study, artificial neural networks are utilized to predict the performance of triple effect series and parallel flow absorption refrigeration systems, with lithium bromide/water as the working fluid. Important parameters such as high generator and evaporator temperatures were varied and their effects on the performance characteristics of the refrigeration unit were observed. Absorption refrigeration systems make energy savings possible because they can use heat energy to produce cooling, in place of the electricity used for conventional vapour compression chillers. In addition, non-conventional sources of energy (such as solar, waste heat, and geothermal) can be utilized as their primary energy input. Moreover, absorption units use environmentally friendly working fluid pairs instead of CFCs and HCFCs, which affect the ozone layer. Triple effect absorption cycles were analysed. Results apply for both series and parallel flow systems. A relative preference for parallel-flow over series-flow is also shown.

  17. Artificial Neural Network Modeling of Forest Tree Growth

    CERN Document Server

    Gordon, C

    1999-01-01

    The problem of modeling forest tree growth curves with an artificial neural network (NN) is examined. The NN parametric form is shown to be a suitable model if each forest tree plot is assumed to consist of several differently growing sub-plots. The predictive Bayesian approach is used in estimating the NN output. Data from the correlated curve trend (CCT) experiments are used. The NN predictions are compared with those of one of the best parametric solutions, the Schnute model. Analysis of variance (ANOVA) methods are used to evaluate whether any observed differences are statistically significant. From a Frequentist perspective the differences between the Schnute and NN approach are found not to be significant. However, a Bayesian ANOVA indicates that there is a 93% probability of the NN approach producing better predictions on average.

  18. CYLINDIRICAL SPUR GEARS DESIGN BASED ON ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    İhsan TOKTAŞ

    2007-03-01

    Full Text Available In this study, testing and training data sets of Artificial Neural Networks(ANNs models have been produced by employing analytical design calculations of cylindrical spur gears. In the input layer, the constraints and requirement values of cylindrical spur gears are used while at the output layer the modules (e.g. the bending and contact stress and the number of tooths are used. These data have been presented to train a multi layered, single directed, hierarchically connected ANNs using Scaled Conjugate Gradient (SCG and Levenberg-Marquardt (LM Back Propagation algorithms with the logistic sigmoid transfer function. The outcomes demonstrated that, the ANN based model have been very successful and the testing data produced very low level of errors. It has been shown that, the ANN based mechanism may be used in the design of cylindrical spur gears instead of analytical calculations.

  19. PREDICTION AND CLASSIFICATION OF THUNDERSTORMS USING ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    AMIT KESARKAR

    2011-05-01

    Full Text Available Natural calamities cause heavy destruction to both life and property. Prediction of such calamities well in advance is inevitable. Prediction and classification of thunderstorms using ArtificialNeural Network (ANN is presented in this paper. The Numerical Weather Prediction (NWP models used today suffer from course resolution and inaccuracy. Two geographical locations are considered for our study namely, Paradeep in the west cost of India and Wollemi National Park, New South Wales, (Australia. ANN has designed to forecasts the occurrence of thunderstorm in these regions. Inputparameter selection is very critical in ANN design, Eight input parameters were identified to train the network. The output nodes clearly classifies the days with and without thunderstorms, thus successfully predicting thunderstorm activity in the specified regions.

  20. Prediction of the plasma distribution using an artificial neural network

    Institute of Scientific and Technical Information of China (English)

    Li Wei; Chen JunFang; Wang Teng

    2009-01-01

    In this work, an artificial neural network (ANN) model is established using a back-propagation training algorithm in order to predict the plasma spatial distribution in an electron cyclotron resonance (ECR) - plasma-enhanced chemical vapor deposition (PECVD) plasma system. In our model, there are three layers: the input layer, the hidden layer and the output layer. The input layer is composed of five neurons: the radial position, the axial position, the gas pressure,the microwave power and the magnet coil current. The output layer is our target output neuron: the plasma density.The accuracy of our prediction is tested with the experimental data obtained by a Langmuir probe, and ANN results show a good agreement with the experimental data. It is concluded that ANN is a useful tool in dealing with some nonlinear problems of the plasma spatial distribution.

  1. Artificial metaplasticity neural network applied to credit scoring.

    Science.gov (United States)

    Marcano-Cedeño, Alexis; Marin-de-la-Barcena, A; Jimenez-Trillo, J; Piñuela, J A; Andina, D

    2011-08-01

    The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons' biological property of metaplasticity. This algorithm is especially efficient when few patterns of a class are available, or when information inherent to low probability events is crucial for a successful application, as weight updating is overemphasized in the less frequent activations than in the more frequent ones. Two well-known and readily available such as: Australia and German data sets has been used to test the algorithm. The results obtained by AMMLP shown have been superior to state-of-the-art classification algorithms in credit scoring.

  2. Practical application of artificial neural networks in the neurosciences

    Science.gov (United States)

    Pinti, Antonio

    1995-04-01

    This article presents a practical application of artificial multi-layer perceptron (MLP) neural networks in neurosciences. The data that are processed are labeled data from the visual analysis of electrical signals of human sleep. The objective of this work is to automatically classify into sleep stages the electrophysiological signals recorded from electrodes placed on a sleeping patient. Two large data bases were designed by experts in order to realize this study. One data base was used to train the network and the other to test its generalization capacity. The classification results obtained with the MLP network were compared to a type K nearest neighbor Knn non-parametric classification method. The MLP network gave a better result in terms of classification than the Knn method. Both classification techniques were implemented on a transputer system. With both networks in their final configuration, the MLP network was 160 times faster than the Knn model in classifying a sleep period.

  3. Offline Arabic Handwriting Recognition Using Artificial Neural Network

    CERN Document Server

    Zaidan, A A; Jalab, Hamid A; Alanazi, Hamdan O; Alnaqeib, Rami

    2010-01-01

    The ambition of a character recognition system is to transform a text document typed on paper into a digital format that can be manipulated by word processor software Unlike other languages, Arabic has unique features, while other language doesn't have, from this language these are seven or eight language such as ordo, jewie and Persian writing, Arabic has twenty eight letters, each of which can be linked in three different ways or separated depending on the case. The difficulty of the Arabic handwriting recognition is that, the accuracy of the character recognition which affects on the accuracy of the word recognition, in additional there is also two or three from for each character, the suggested solution by using artificial neural network can solve the problem and overcome the difficulty of Arabic handwriting recognition.

  4. Extraction of Symbolic Rules from Artificial Neural Networks

    CERN Document Server

    Kamruzzaman, S M

    2010-01-01

    Although backpropagation ANNs generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions cannot be explained as those of decision trees. In many applications, it is desirable to extract knowledge from trained ANNs for the users to gain a better understanding of how the networks solve the problems. A new rule extraction algorithm, called rule extraction from artificial neural networks (REANN) is proposed and implemented to extract symbolic rules from ANNs. A standard three-layer feedforward ANN is the basis of the algorithm. A four-phase training algorithm is proposed for backpropagation learning. Explicitness of the extracted rules is supported by comparing them to the symbolic rules generated by other methods. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy. Extensive experimental studies on several benchmarks classification ...

  5. High power fuel cell simulator based on artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Chavez-Ramirez, Abraham U.; Munoz-Guerrero, Roberto [Departamento de Ingenieria Electrica, CINVESTAV-IPN. Av. Instituto Politecnico Nacional No. 2508, D.F. CP 07360 (Mexico); Duron-Torres, S.M. [Unidad Academica de Ciencias Quimicas, Universidad Autonoma de Zacatecas, Campus Siglo XXI, Edif. 6 (Mexico); Ferraro, M.; Brunaccini, G.; Sergi, F.; Antonucci, V. [CNR-ITAE, Via Salita S. Lucia sopra Contesse 5-98126 Messina (Italy); Arriaga, L.G. [Centro de Investigacion y Desarrollo Tecnologico en Electroquimica S.C., Parque Tecnologico Queretaro, Sanfandila, Pedro Escobedo, Queretaro (Mexico)

    2010-11-15

    Artificial Neural Network (ANN) has become a powerful modeling tool for predicting the performance of complex systems with no well-known variable relationships due to the inherent properties. A commercial Polymeric Electrolyte Membrane fuel cell (PEMFC) stack (5 kW) was modeled successfully using this tool, increasing the number of test into the 7 inputs - 2 outputs-dimensional spaces in the shortest time, acquiring only a small amount of experimental data. Some parameters could not be measured easily on the real system in experimental tests; however, by receiving the data from PEMFC, the ANN could be trained to learn the internal relationships that govern this system, and predict its behavior without any physical equations. Confident accuracy was achieved in this work making possible to import this tool to complex systems and applications. (author)

  6. Retrieving Atmospheric Precipitable Water Vapor Using Artificial Neural Network Approach

    Directory of Open Access Journals (Sweden)

    Wang Xin

    2013-07-01

    Full Text Available Discussing of water vapor and its variation is the important issue for synoptic meteorology and meteorology. In physical Atmospheric, the moisture content of the earth atmosphere is one of the most important parameters, it is hard to represent water vapor because of its space-time variation. High-spectral resolution Atmospheric Infrared Sounder (AIRS data can be used to retrieve the small scale vertical structure of air temperature, which provided a more accurate and good initial field for the numerical forecasting and the large-scale weather analysis. This paper proposes an artificial neural network to retrieve the clear sky atmospheric radiation data from AIRS and comparing with the AIRS Level-2 standard product, and gain a good inversion results.

  7. Backpropagation Artificial Neural Network To Detect Hyperthermic Seizures In Rats

    Directory of Open Access Journals (Sweden)

    Rakesh Kumar Sinha

    2003-02-01

    Full Text Available A three-layered feed-forward back-propagation Artificial Neural Network was used to classify the seizure episodes in rats. Seizure patterns were induced by subjecting anesthetized rats to a Biological Oxygen Demand incubator at 45-47ºC for 30 to 60 minutes. Selected fast Fourier transform data of one second epochs of electroencephalogram were used to train and test the network for the classification of seizure and normal patterns. The results indicate that the present network with the architecture of 40-12-1 (input-hidden-output nodes agrees with manual scoring of seizure and normal patterns with a high recognition rate of 98.6%.

  8. Forecasting of IBOVESPA returns using feedforward evolutionary artificial neural networks

    Directory of Open Access Journals (Sweden)

    Edgar Leite dos Santos Filho

    2011-12-01

    Full Text Available Facing the challenges of anticipating financial market uncertainties and movements, and the necessity of taking buy or sell decisions supported by rational methods, market traders found in statistics and econometrics methods, the base to support their decisions. In several scientific papers about forecasting financial time series, method selection keeps as central concern. This paper compares the performance of evolutionary feedforward artificial neural network (EANN and an AR+GARCH model, for one step ahead forecasting of IBOVESPA returns. The EANN is trained by self-adapting differential evolution algorithm and AR+GARCH model is adjusted to be used as performance reference. The root mean square error (RMSE and U-Theil inequality coefficient were used as performance metrics. Simulation results showed  EANN feedforward achieved better results, fit better and captured the nonlinear behavior of returns.

  9. The application of artificial neural networks in indirect cost estimation

    Science.gov (United States)

    Leśniak, Agnieszka

    2013-10-01

    Estimating of the costs of construction project is one of the most important task in the management of the project. The total costs can be divided into direct costs that are related to executing the works, and indirect costs that accompany delivery. A precise costs estimation is usually a highly labour and time-intensive task especially when using manual calculation methods. This paper presents Artificial Neural Network (ANN) approach to predicting index of indirect cost of construction projects in Poland. A quantitative study was undertaken on the factors conditioning indirect costs of polish construction projects and a determination was made of the actual costs incurred by enterprises during project implementation. As a result of these studies, a data set was assembled covering 72 real-life cases of building projects constructed in Poland.

  10. Artificial neural networks in foodstuffs: a critical review

    Directory of Open Access Journals (Sweden)

    S. Goyal

    2012-11-01

    Full Text Available This paper provides a critical review of literature concerning the artificial neural networks (ANN in foodstuffs. The main aim is to provide background information, motivation for applications and an exposition to the methodologies employed in the development of ANN techniques in foodstuffs. This review includes that all the latest works on the application of ANN to foodstuffs which have been reported excellently with positive and encouraging results. This review paper highlights the methodologies and algorithms employed for ANN models suitable for various foodstuffs, viz., avocados, tomatoes, cherries, grape, mosambi juice, apple juice, chicken nuggets, pistachio nuts, potato chips, kalakand, cakes, processed cheese, butter, milk and other foodstuffs. This review paper would be very beneficial for those working in food industry, academicians, students, researchers, scientists, factories manufacturing the food products and regulatory authorities, as it provides comprehensive latest information.

  11. Artificial Neural Networks in Fruits: A Comprehensive Review

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2014-04-01

    Full Text Available This review discusses the application of artificial neural networks (ANN modeling in fruits. It covers all fruits in which ANN modeling has been applied. ANN is quite a new and easy computational modeling approach used for prediction, which has become popular and accepted by food industry, researchers, scientists and students. ANNs have been applied in almost every field of science and technology, viz., speech synthesis & recognition, pattern classification, adaptive interfaces between humans & complex physical systems, clustering, function approximation, image data compression, non-linear system modeling, associative memory, combinatorial optimization, control and several others, as they have proved valuable tools for obtaining the required output. ANN provides an exciting alternative method for solving a variety of problems in different areas of science and engineering. The aim of this communication is to discover the recent advances of ANN technology implemented in fruits, and discuss the critical role that ANN plays in predictive modelling.

  12. Searching for turbulence models by artificial neural network

    CERN Document Server

    Gamahara, Masataka

    2016-01-01

    Artificial neural network (ANN) is tested as a tool for finding a new subgrid model of the subgrid-scale (SGS) stress in large-eddy simulation. ANN is used to establish a functional relation between the grid-scale (GS) flow field and the SGS stress without any assumption of the form of function. Data required for training and test of ANN are provided by direct numerical simulation (DNS) of a turbulent channel flow. It is shown that ANN can establish a model similar to the gradient model. The correlation coefficients between the real SGS stress and the output of ANN are comparable to or larger than similarity models, but smaller than a two-parameter dynamic mixed model.

  13. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    International Nuclear Information System (INIS)

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  14. Prediction of Austenite Formation Temperatures Using Artificial Neural Networks

    Science.gov (United States)

    Schulze, P.; Schmidl, E.; Grund, T.; Lampke, T.

    2016-03-01

    For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels.

  15. Comparison of sonar discrimination: dolphin and an artificial neural network.

    Science.gov (United States)

    Au, W W

    1994-05-01

    The capability of an echolocating dolphin to discriminate differences in the wall thickness of cylinders (3.81 cm o.d. and 12.7 cm length) was determined by Au and Pawloski [J. Comp. Physiol. A 170, 41-47 (1992)]. The dolphin was required to discriminate a standard target from comparison targets of differing wall thicknesses. Performance varied from 96% to 56% correct depending on the wall thickness of the comparison targets. The 75% correct threshold was determined to be wall thickness differences of -0.23 mm for comparison targets with thinner walls and +0.27 mm for comparison targets with thicker walls than the standard. The dolphin performance was unchanged in the presence of artificial broadband masking noise until the echo-energy-to-noise ratio fell below approximately 15 dB. A counterpropagation artificial neural network was used to examine broadband echo features from the same cylinders. Features of the echoes were determined by passing them through a filter bank of constant-Q filters. Echo features of the standard and each comparison target were analyzed in pairs by a neural network having two output nodes. Twenty echoes per target were used in the training set and 30 additional echoes per target were used in the test set. For the noise free condition, the network performed at a comparable level to the dolphin for Q values between 4 and 5. In the presence of noise, Q values between 7 and 8 were needed before the network could perform at a comparable level to the dolphin for echo-energy-to-noise ratios of 10 and 15 dB.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:8207144

  16. Didactic Strategy Discussion Based on Artificial Neural Networks Results.

    Science.gov (United States)

    Andina, D.; Bermúdez-Valbuena, R.

    2009-04-01

    Artificial Neural Networks (ANNs) are a mathematical model of the main known characteristics of biological brian dynamics. ANNs inspired in biological reality have been useful to design machines that show some human-like behaviours. Based on them, many experimentes have been succesfully developed emulating several biologial neurons characteristics, as learning how to solve a given problem. Sometimes, experimentes on ANNs feedback to biology and allow advances in understanding the biological brian behaviour, allowing the proposal of new therapies for medical problems involving neurons performing. Following this line, the author present results on artificial learning on ANN, and interpret them aiming to reinforce one of this two didactic estrategies to learn how to solve a given difficult task: a) To train with clear, simple, representative examples and feel confidence in brian generalization capabilities to achieve succes in more complicated cases. b) To teach with a set of difficult cases of the problem feeling confidence that the brian will efficiently solve the rest of cases if it is able to solve the difficult ones. Results may contribute in the discussion of how to orientate the design innovative succesful teaching strategies in the education field.

  17. SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    WANG Xie-kang; HUANG Er; CUI Peng

    2003-01-01

    Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-ful in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.

  18. Advances in spectral analysis using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez, M.; Vigneron, V.

    1995-12-31

    Artificial Neural networks (ANNs) have a powerful representational capacity and ability to handle with any multi-input multi-output mapping problem, e.g. in clustering, pattern recognition and identification areas, particularly when combined with some a priori knowledge and statistical point of view. They can be useful in spectrometry for the uranium enrichment methods by examples, where numerous approaches like models fitting or experts analysis are limited. These depends on the radiation measured: the methods most widely used developed over the past 20 years were based on the counting of the 185.7-keV peak with a sodium iodide scintillation detector or the 163.4-keV peak of {sup 235} U. But these methods depend critically of the source-detector geometry. A means of improving the above conventional methods is to reduce the region of interest: it is possible by focusing at the region called K{alpha}X where the three elementary components are present. The measurement of these components in mixtures leads to the isotope ratio {sup 235} U / ({sup 235} U + {sup 236} U + {sup 238} U). In this paper we explore statistical orientations and their consequences on `neural` parameters. We show this decisions are induced by a log-linear model, a special case of a GLIM (Generalized LInear Model) and correspond to a Maximum Likelihood Estimation problem. (authors). 15 refs., 7 figs., 2 tabs.

  19. Reliability and risk analysis using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Robinson, D.G. [Sandia National Labs., Albuquerque, NM (United States)

    1995-12-31

    This paper discusses preliminary research at Sandia National Laboratories into the application of artificial neural networks for reliability and risk analysis. The goal of this effort is to develop a reliability based methodology that captures the complex relationship between uncertainty in material properties and manufacturing processes and the resulting uncertainty in life prediction estimates. The inputs to the neural network model are probability density functions describing system characteristics and the output is a statistical description of system performance. The most recent application of this methodology involves the comparison of various low-residue, lead-free soldering processes with the desire to minimize the associated waste streams with no reduction in product reliability. Model inputs include statistical descriptions of various material properties such as the coefficients of thermal expansion of solder and substrate. Consideration is also given to stochastic variation in the operational environment to which the electronic components might be exposed. Model output includes a probabilistic characterization of the fatigue life of the surface mounted component.

  20. Advances in spectral analysis using artificial neural networks

    International Nuclear Information System (INIS)

    Artificial Neural networks (ANNs) have a powerful representational capacity and ability to handle with any multi-input multi-output mapping problem, e.g. in clustering, pattern recognition and identification areas, particularly when combined with some a priori knowledge and statistical point of view. They can be useful in spectrometry for the uranium enrichment methods by examples, where numerous approaches like models fitting or experts analysis are limited. These depends on the radiation measured: the methods most widely used developed over the past 20 years were based on the counting of the 185.7-keV peak with a sodium iodide scintillation detector or the 163.4-keV peak of 235 U. But these methods depend critically of the source-detector geometry. A means of improving the above conventional methods is to reduce the region of interest: it is possible by focusing at the region called KαX where the three elementary components are present. The measurement of these components in mixtures leads to the isotope ratio 235 U / (235 U + 236 U + 238 U). In this paper we explore statistical orientations and their consequences on 'neural' parameters. We show this decisions are induced by a log-linear model, a special case of a GLIM (Generalized LInear Model) and correspond to a Maximum Likelihood Estimation problem. (authors). 15 refs., 7 figs., 2 tabs