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检索条件"主题词=Backpropagation Algorithms"

共 1893 条记录，以下是301-310 订阅

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CAD modeling of complex resonant frequencies of a rectangular microstrip patch with a superstrate using complex backpropagation algorithm

CAD modeling of complex resonant frequencies of a rectangula...

引用

IEEE Applied Electromagnetics Conference (AEMC)

作者： Dhruba C. Panda Prabhat K. Patnaik Department of Electronic Science Berhampur University Odisha India Dept. of ECE S.O.E.T Centurion University of Technology and Management Paralakhemundi Odisha India

This paper presents application of complex neural network for calculating complex resonating frequency of microstrip patch antenna on superstrate. The results obtained from neural network agrees well with the theoreti... 详细信息

关键词： Resonant frequency Biological neural networks backpropagation backpropagation algorithms Neurons Microstrip antennas Feeds

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Automatic keyphrases extraction from document using backpropagation

Automatic keyphrases extraction from document using backprop...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jia-Bing Wang Hong Peng Jing-Song Hu School of Computer Science and Engineering South China University of Technology Guangzhou China

Automatic keyphrase extraction from documents is a task with many applications in information retrieval and natural language processing. Previously, Several keyphrase extraction methods have been proposed based on different techniques. In this paper a keyphrase extraction approach based on backpropagation is proposed. In order to determine whether a phrase is a keyphrase or not, the following features of a phrase in a given document are adopted: its term frequency TF and inverted document frequency IDF, whether or not it appears in the title or headings (subheadings) of the given document, and its distribution in the paragraphs of the given document. The algorithm is evaluated by the standard information retrieval metrics of precision and recall and human assessment. Experiment results show that this approach is competitive with other known methods.

关键词： Data mining Information retrieval Natural language processing Databases Frequency measurement Particle measurements Computer science Application software backpropagation algorithms Humans

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backpropagation Learning Method with Interval Type-2 Fuzzy Weights in Neural Networks

Backpropagation Learning Method with Interval Type-2 Fuzzy W...

引用

International Joint Conference on Neural Networks

作者： Fernando Gaxiola Patricia Melin Fevrier Valdez Oscar Castillo Tijuana Institute of Technology Tijuana México

ISBN: (纸本)9781467361279

In this paper a neural network learning method with lower and upper type-2 fuzzy weight adjustment is proposed. The general mathematical analysis of the proposed learning method architecture and the adaptation of the interval type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that manage weight adaptation and especially type-2 fuzzy weights. In this paper the neural network architecture managing lower and upper type-2 fuzzy weights and the obtained lower and upper final results are presented. The proposed approach is applied to a case of Mackey-Glass time series prediction.

关键词： Neural Networks Type-2 Fuzzy Weights backpropagation Algorithm Type-2 fuzzy system Neural network backpropagation algorithms Learning methods Fuzzy back propagation

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backpropagation networks including time delay elements (BPD)

Backpropagation networks including time delay elements (BPD)

引用

International Joint Conference on Neural Networks (IJCNN)

作者： M. Nishi J. Furuya T. Nakamura Faculty of Education Yamaguchi University Yamaguchi Japan Graduate School of Information Sciences University of Tohoku Sendai Japan

Multilayered neural networks are applied to many problems. These networks need many learning iterations and can not directly handle the time sequence signals. In this paper the authors propose the backpropagation network (BPD) with time delay elements in the feedback. The backpropagation network described does not need as many learning iterations and is usable as a nonlinear filter.

关键词： Delay effects Neural networks Nonlinear filters Signal processing Equations Neurofeedback backpropagation algorithms Output feedback

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On the implementation of backpropagation on the Alex AVX-2 parallel system

On the implementation of backpropagation on the Alex AVX-2 p...

引用

International Conference on Neural Networks

作者： H.M. Abbas M.M. Bayoumi Department of Electrical and Computer Engineering Queen's University Kingston ON Canada

Training backpropagation (BP) networks is a time-consuming process especially on sequential machines. This has motivated the use of parallel architectures to decrease the processing time required for training. In this paper the implementation of the BP algorithm on the Alex AVX-2 MIMD machine is investigated. Due to the high communication time caused by sending and receiving network information and due to the overhead of the message passing process, the conventional use of block-BP is not appropriate for this particular machine. Increasing the processing load of the workers with respect to the communication load will definitely increase the speedup factor. Here, we propose a block-update learning method for BP which reduces the communication time and produces results similar to those obtained with parallel block-BP.

关键词： Message passing Parallel architectures backpropagation algorithms Network topology Master-slave Encoding Learning systems Neural networks Feedforward neural networks Large-scale systems

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Effect of activation function symmetry on training of SFFANNs with the backpropagation algorithm

Effect of activation function symmetry on training of SFFANN...

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International Conference on (ISCO) Intelligent Systems and Control

作者： Pravin Chandra Amit Prakash Singh Ruchi Sehrawat University School of Information and Communications Technology Guru Gobind Singh Indraprastha University New Delhi INDIA

ISBN: (纸本)9781467378086

On 17 learning task (12 function approximation and 5 real life regression problems), we compare the efficiency and efficacy of using asymmetric or anti-symmetric activation functions in sigmoidal feedforward artificial neural network training and usage. The result obtained in the experiment allows us to conclude that for networks trained using the batch update variant of the backpropagation algorithm, the usage of antisymmetric activation functions may give better performance is some cases, but in a few cases networks using antisymmetric activation functions may give better performance and in majority of cases the performance of networks using anti-symmetric or asymmetric activation are equivalent. Thus a clear preference for anti-symmetric activation functions cannot be made.

关键词： Training Glass Function approximation Feedforward neural networks backpropagation algorithms MATLAB

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Accurate initialization of neural network weights by backpropagation of the desired response

Accurate initialization of neural network weights by backpro...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： D. Erdogmus O. Fontenla-Romero J.C. Principe A. Alonso-Betanzos E. Castillo R. Jenssen Electrical Engineering Department University of Florida Gainesville FL USA Department of Computer Science University of A Coruna Coruna Spain Department of Applied Mathematics University of Cantabria Santander Spain

Proper initialization of neural networks is critical for a successful training of its weights. Many methods have been proposed to achieve this, including heuristic least squares approaches. In this paper, inspired by these previous attempts to train (or initialize) neural networks, we formulate a mathematically sound algorithm based on backpropagating the desired output through the layers of a multilayer perceptron. The approach is accurate up to local first order approximations of the nonlinearities. It is shown to provide successful weight initialization for many data sets by Monte Carlo experiments.

关键词： Neural networks Least squares approximation backpropagation algorithms Neurons Least squares methods Multilayer perceptrons Monte Carlo methods Computer science Mathematics Multi-layer neural network

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The backpropagation algorithm applied to protective relaying

The backpropagation algorithm applied to protective relaying

引用

International Conference on Neural Networks

作者： D.V. Coury D.C. Jorge Electrical Engineering Department University of São Paulo Sao Paulo Brazil

Distance relays have attracted considerable attention for the protection of transmission lines. They are usually designed on the basis of fixed settings. Therefore, the reach of such relays is affected by the changing network conditions. The implementation of a pattern recognizer for power system diagnosis can provide great advances in the protection field. This paper demonstrates the use of an artificial neural network as a pattern classifier for distance relay operation. The backpropagation algorithm is utilized for the learning process. The scheme utilizes the magnitude of three phase voltage and current phasors as inputs. An improved performance with the use of an artificial neural network approach is experienced once the relay can operate correctly, keeping the reach when faced with different fault conditions as well as network configuration changes.

关键词： backpropagation algorithms Protective relaying Artificial neural networks Power system protection Power system relaying Power transmission lines Transmission line theory Pattern recognition Digital relays Voltage

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Embedding discriminant directions in backpropagation

Embedding discriminant directions in backpropagation

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IEEE Southeastcon

作者： G.M. Georgiou C. Koutsougeras Center of Automation and Autonomous Complex Systems Computer Science Department Tulane University New Orleans LA USA

A two-phase backpropagation algorithm is presented. In the first phase the directions of the weight vectors of the first hidden layer are constrained to remain in directions suitably chosen by pattern recognition, data compression, or speech and image processing techniques. Then, the constraints are removed and the standard backpropagation algorithm takes over to further minimize the error function. The first phase swiftly situates the weight vectors in a good position which can serve as the initialization of the standard backpropagation algorithm. The generality of its application, its simplicity, and the shorter training time it requires, makes this approach attractive.< >

关键词： backpropagation algorithms Vectors Data compression Speech processing Speech analysis Automation Computer science Pattern recognition Feedforward systems Neural networks

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Mapping of backpropagation learning onto distributed memory multiprocessors

Mapping of backpropagation learning onto distributed memory ...

引用

IEEE International Conference on algorithms and Architectures for Parallel Processing (ICAP)

作者： S. Mahapatra R.N. Mahapatra Department of Electronics & Electrical Communication Engineering Indian Institute of Technology Kharagpur India Department of Computer Science Engineering & Applications Regional Engineering College Orissa India

This paper presents a mapping scheme for parallel pipelined execution of the backpropagation Learning Algorithm on distributed memory multiprocessors (DMMs). The proposed implementation exhibits training set parallelism that involves batch updating. Simple algorithms have been presented, which allow the data transfer involved in both forward and backward executions phases of the backpropagation algorithm to be carried out with a small communication overhead. The effectiveness of our mapping has been illustrated, by estimating the speedup of a proposed implementation on an array of T-805 transputers.< >

关键词： Multi-layer neural network Parallel processing backpropagation algorithms Neural networks Neurons Performance analysis Parallel architectures Visualization Computer science Application software

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