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检索条件"主题词=Back propagation Algorithm"

共 277 条记录，以下是151-160 订阅

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High Impedance Arcing Fault Detection in MV Networks Using Discrete Wavelet Transform and Artificial Neural Networks

High Impedance Arcing Fault Detection in MV Networks Using D...

引用

International Conference on Green Technologies (ICGT)

作者： Vijayachandran, Gayathri Mathew, Bobin. K. Amal Jyothi Coll Engn Dept Elect & Elect Kottayam Kerala India

ISBN: (纸本)9781467326360;9781467326353

Arcing faults in transmission networks are caused when a current carrying conductor makes an unwanted electrical contact with ground or is temporarily short circuited with another current carrying conductor through a high impedance medium. High impedance arcing faults restricts the flow of current below the detection level of the protective devices and hence cannot be detected by conventional relays. In this paper a new method is proposed for the detection of arcing faults due to leaning trees in medium voltage (MV) networks. Firstly, an arc model is developed in order to reproduce the fault circumstances. Then based on a fault detection algorithm the fault features are extracted using a signal processing technique called Discrete Wavelet Transform (DWT). The proposed algorithm is implemented in a simple MV network to identify the faulty phase and in a feeder network to identify both the faulty phase and feeder. Further the results obtained using DWT is validated with the help of Artificial Neural Networks (ANN). The results obtained above validate the effectiveness of the proposed methodology.

关键词： High impedance fault Arc model Universal Arc representation Discrete Wavelet Transform Absolute sum Artificial Neural Networks back propagation algorithm

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Artificial Neural Network Approach for Modeling of Photovoltaic Module 4

Artificial Neural Network Approach for Modeling of Photovolt...

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4th International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Khera, Neeraj Shukla, Divya Kumar, Vipul Amity Univ ASET Dept ECE Noida Uttar Pradesh India

ISBN: (纸本)9781509027972

In this paper, the artificial neural network approach to model photovoltaic (PV) module of photovoltaic system is presented. The main objective of this paper is to evaluate the performance of photovoltaic module of PV system during its standstill and single-axis tracking mode operation and also to obtain the variation of open circuit voltage of PV module with solar irradiance and temperature using the neural network approach. The open circuit voltage and the output power of photovoltaic module depend on irradiance and temperature but their relationship is nonlinear and therefore cannot be empirically evaluated. For this purpose the artificial neural network is applied for determining open circuit voltage of photovoltaic module at different operating temperature and irradiance conditions. The data values of open circuit voltage for target photovoltaic module at different operating temperature and irradiance are obtained experimentally at standstill and tracking mode operation using a microcontroller based test bed. Finally, using the input datasets obtained from test bed, the back propagation training algorithm for artificial neural network is implemented to determine the open circuit voltage of the module at a particular operating temperature and irradiance. The evaluated value at test condition is compared with practically obtained value to verify the validity of proposed method.

关键词： artificial neural network back propagation algorithm microcontroller Photovoltaic module sensors test bed

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Short Term Load Forecasting by Clustering Technique based on Daily Average and Peak Loads

Short Term Load Forecasting by Clustering Technique based on...

引用

General Meeting of the IEEE-Power-and-Energy-Society

作者： Jain, Amit Satish, B. Int Inst Informat Technol Power Syst Res Ctr Hyderabad Andhra Pradesh India

ISBN: (纸本)9781424442409

A novel clustering based Short Term Load Forecasting (STLF) using Artificial Neural Network (ANN) for forecasting the next day load is presented in this paper. The input parameters considered for prediction are load, temperature and day of the week. The daily average load of each day for all the training patterns and testing patterns is calculated and the patterns are clustered using a threshold value between the daily average load of the testing pattern and the daily average load of the training patterns. Similarly, the training patterns are clustered using a threshold value between the daily peak load of the testing pattern and the daily peak load of the training pattern. The ANN is trained with back propagation algorithm and tested. The results for different cases - without clustering, clustering based on daily aver-age load, clustering based on daily peak load are presented and the results show that clustering technique provide better results.

关键词： Artificial Neural Network back propagation algorithm Clustering Short Term Load Forecasting

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Observer-Based Adaptive Neural Network Control for a Class of Uncertain Nonlinear Systems 22

Observer-Based Adaptive Neural Network Control for a Class o...

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22nd Iranian Conference on Electrical Engineering (ICEE)

作者： Esfandiari, K. Abdollahi, F. Talebi, H. A. Amirkabir Univ Technol Dept Elect Engn Ctr Excellence Control & Robot Tehran Iran

ISBN: (纸本)9781479944095

This paper deals with the problem of designing an observer-based adaptive tracking controller for a class of uncertain nonlinear systems. A neural network-based observer estimates states of the system and a neural network-based controller is designed to approximate input control signal. The estimated states by the observer are inputs of the controller and two neural networks (NNs) interact together such that the output of the system tracks the desired trajectory. Unlike most of the previous adaptive observers and controllers which employed linear in parameter neural networks (LPNNs), the proposed observer and controller are based on the nonlinear in parameter neural networks (NLPNNs). Hence, the proposed scheme supports global approximation property and is applicable to the systems with high degrees of nonlinearity. NNs learning rules are developed based on the well-known back propagation (BP) algorithm which has been proven to be the most relevant updating rule for control problems and despite most of the previous work by adding robustifying terms to the learning rules uniformly ultimately boundedness (UUB) of all signals of the closed-loop system is guaranteed by Lyapunov's direct method. Finally, simulations performed on the ''generalized pendulum'' nonlinear system to demonstrate the effectiveness and performance of the proposed observer-based tracking controller scheme.

关键词： Adaptive control back propagation algorithm neural networks nonlinear system state observer

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Design an Electronic System Use the Audio Fingerprint to Access Virtual Classroom Using Artificial Neural Networks 2

Design an Electronic System Use the Audio Fingerprint to Acc...

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2nd International Conference Computer Communications Control Technology

作者： Hammad, Mahgoub H. Mohammed, Alsadig Eldow, Moawia E. Univ Khartoum Dept Comp Sci Khartoum Sudan Bisha Univ Bisha Saudi Arabia Salman Bin AbdAlziz Univ Dept Comp Sci Al Kharj Sudan Univ Khartoum Khartoum Sudan

ISBN: (纸本)9781479979523

This study aims at designing and developing an electronic system that depends on the Audio Fingerprint to access the learning management system (Blackboard) by students of King Khalid University, as a complement to the current system based on username and password only. [1] The vulnerability of the former used system is represented in the possible accessibility of non authorized peoples to the system who enjoy all available academic services through the utilization of the tests and assignments and participate in the forums and discussions. They deceive the subject teacher. This affects the credibility of the final grades and students' assessment. The system proposed by the researcher works to avoid this gap where it becomes possible to check the students' identity and the authorized users by verifying the Audio Fingerprint through certain words the person utters, using some phrases and replications. The program, then, checks the imprint of the student's voice through an algorithm matching which was designed by the researcher. The results shows that the percentages of matching to the recorded sounds were 94% in ordinary circumstances and 98.5% in an equipped studio. The study recommends the use of the system in all virtual learning centers. In addition, the biometrics can be developed and utilized for academic authentication purposes.

关键词： Biometrics Audio Finger print Artificial Neural Networks back propagation algorithm Virtual Classrooms

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Infrared Face Recognition Using Neural Networks and HOG-SVM 3

Infrared Face Recognition Using Neural Networks and HOG-SVM

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3rd International Conference on Bio-engineering for Smart Technologies (BioSMART)

作者： Cherifi, Dalila Kaddari, Redouane Zair, Hamza Nait-Ali, Amine Univ Boumerdes Inst Elect & Elect Engn Boumerdes Algeria Paris Est Creteil UPEC LiSSi EA 3956 Creteil France

ISBN: (纸本)9781728135786

With the rapid development of computer vision applications, infrared face recognition is receiving much attention because of miniscule sensitivity to the face variations due to illumination changes. The infrared image is merely a gray level image but the recognition is interestingly good enough to look at. In this article we present an infrared face recognition system where we used two methods and two databases to train and test our system. We used the Histogram of Gradient (HOG) algorithm along with the Support Vector Machine (SVM) classifier as a first method. In the second method, we implemented the backpropagation algorithm. Although the results of the HOG-SVM were very promising and we were able to get a better results compared with a previous work, neural network gave us a perfect results by using the same number of training examples.

关键词： Face Recognition HOG-SVM back propagation algorithm Biometric

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Clustering based Short Term Load Forecasting using Artificial Neural Network

Clustering based Short Term Load Forecasting using Artificia...

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IEEE/PES Power Systems Conference and Exposition

作者： Jain, Amit Satish, B. Int Inst Informat Technol Power Syst Res Ctr Hyderabad Andhra Pradesh India

ISBN: (纸本)9781424438105

A novel clustering based Short Term Load Forecasting (STLF) using Artificial Neural Network (ANN) to forecast the 48 half hourly loads for next day is presented in this paper. The proposed architecture uses the historical load and temperature to forecast the next day load. It is trained using back propagation algorithm and tested. The daily average load of each day for all the training patterns and testing patterns is calculated and the patterns are clustered using a threshold value between the daily average load of the testing pattern and the daily average load of the training patterns. The results obtained from neural network are presented and the results show that the clustering based approach is more accurate.

关键词： Artificial Neural Network back propagation algorithm Clustering Short Term Load Forecasting

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Hand Drawn Optical Circuit Recognition 7th

Hand Drawn Optical Circuit Recognition

引用

7th International Conference on Intelligent Human Computer Interaction (IHCI)

作者： Rabbani, Mahdi Khoshkangini, Reza Nagendraswamy, H. S. Conti, Mauro Univ Mysore Dept Comp Sci Mysore 570005 Karnataka India Univ Padua Dept Comp Sci I-35100 Padua Italy

Electrical diagram is foundation of studies in electrical science. A circuit diagram convey many information about the system. Behind any device there are plenty of electrical ingredients which perform their specific tasks, today all the electrical software tools failed to effectively convert the information automatically from a circuit image diagram to digital form. Hence electrical engineers should manually enter all information into computers, and this process takes time and bring errors with high probability. Moreover, when the diagram is hand drawn, the problem is more complicated for any electrical analysis. Thus, in this paper we propose a new method using Artificial Neural Network (ANN) to make a machine that can directly read the electrical symbols from a hand drawn circuit image. The recognition process involves two steps: first step is feature extraction using shape based features, and the second one is a classification procedure using ANN through a back propagation algorithm. The ANN was trained and tested with different hand drawn electrical images. The results show that our proposal is viable and brings good performances. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Optical circuit recognition electrical circuit moments of image artificial neural network back propagation algorithm

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Study of Digital Character Recognition Based on BP Neural Networks

Study of Digital Character Recognition Based on BP Neural Ne...

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2nd International Conference on Measurement, Instrumentation and Automation (ICMIA 2013)

作者： Yuan, Shuai Zhang, Guo Yun Wu, Jian Hui Guo, Long Yuan Hunan Inst Sci & Technol Dept Informat & Commun Engn Yueyang 414006 Hunan Peoples R China

ISBN: (纸本)9783037857502

A digital character recognition method is presented based on BP Neural Network. This paper preprocesses the digital character image and extracts character feature, then uses BP Neural Network to recognize digital character. back propagation algorithm seeks network weights to minimize training error in the solution space. A network with hidden layer is created at first, then an input sample vector is sent to network input terminal and the square error E between output values and training sample object output values is calculated. Above process is repeated for input samples of training sets until the error is reduced within the limits of the threshold. The results show that the method presented has good accuracy, quick speed and strong robustness for realtime application.

关键词： Digital character recognition back propagation algorithm Training sample BP Neural Network

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ANFIS Based Intelligent Control of Vector Controlled Induction Motor Drive

ANFIS Based Intelligent Control of Vector Controlled Inducti...

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2nd International Conference on Emerging Trends in Engineering and Technology (ICETET09)

作者： Gupta, R. A. Kumar, Rajesh Surjuse, Rajesh S. MNIT Dept Elect Engn Jaipur Rajasthan India

ISBN: (纸本)9781424452507

This paper presents Adaptive Neuro-Fuzzy Inference System (ANFIS) based intelligent control of vector controlled induction motor drive. The proposed intelligent control scheme consists of sensorless adaptive neuro-fuzzy speed controller with speed estimation based on adaptive neuro-fuzzy inverse model. The proposed neuro-fuzzy speed controller incorporates fuzzy logic algorithm with a five-layer artificial neural network (ANN) structure. The conventional PI speed controller is replaced by Adaptive Neuro-Fuzzy Inference System (ANFIS), which tunes the fuzzy inference system with hybrid learning algorithm. This makes fuzzy system to learn. The structure of speed estimator is also same as speed controller but it incorporates inverse model identification algorithm for speed estimation. The performance of the proposed scheme is investigated at different operating conditions. The simulation study indicates robustness and suitability of drive for high performance drives applications.

关键词： Adaptive Neuro-Fuzzy Inference System (ANFIS) back propagation algorithm hybrid learning algorithm inverse model speed estimator

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