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检索条件"主题词=Backpropagation algorithms"

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Exponentiated backpropagation algorithm for multilayer feedforward neural networks

Exponentiated backpropagation algorithm for multilayer feedf...

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International Conference on Neural Information Processing

作者： N. Srinivasan V. Ravichandran K.L. Chan J.R. Vidhya S. Ramakirishnan S.M. Krishnan Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering Sriperumbudur India Department of Electronics and Communication Engineering Biomedical Engineering Research Centre School of Electrical and Electronics Engineering Nanyang Technological University Singapore Department of Mathematics and Computer Applications Sri Venkateswara College of Engineering Sriperumbudur India Department of Mathematics and Computer Applications Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur India

ISBN: (纸本)9810475241

The gradient descent backpropagation learning algorithm is based on minimizing the mean square error. An alternate approach to gradient descent is the exponentiated gradient descent algorithm which minimizes the relative entropy. Exponentiated gradient descent applied to backpropagation is proposed for a multilayer feedforward neural network. The learning rules for changing weights in the output layer as well the hidden layer neurons in the network are developed. Simulations were performed to explore the convergence and learning of the backpropagation algorithm with exponentiated gradient descent. Accuracy obtained with exponentiated gradient descent back propagation was comparable to the gradient descent back propagation while convergence was faster. The results show that exponentiated gradient descent can be extended to a multilayer feedforward neural network and used in pattern classification applications.

关键词： Multi-layer neural network backpropagation algorithms Neural networks Feedforward neural networks Neurons Biomedical engineering Feeds Mean square error methods Convergence Pattern classification

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backpropagation Modification in Monte-Carlo Game Tree Search

Backpropagation Modification in Monte-Carlo Game Tree Search

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International Symposium on Intelligent Information Technology Application, IITA

作者： Fan Xie Zhiqing Liu Jiu-Ding Computer Go Research Institute Beijing University of Posts and Telecommunications Beijing China

The Algorithm UCT, proposed by Kocsys et al, which apply multi-armed bandit problem into the tree-structured search space, achieves some remarkable success in some challenging fields. For UCT algorithm, Monte-Carlo simulations are performed with the guidance of UCB1 formula, which are averaged to evaluate a specified action. We observe that, as more simulations are performed, later ones usually lead to more accurate results, partly because the level of the search used in the later simulation is deeper and partly because more results are available to direct subsequent simulations. This paper presents a new method to improve the performance of UCT algorithm by increasing the feedback value of the later simulations. And the experimental results in the classical game Go show that our approach increases the performance of Monte-Carlo simulations significantly when exponential models are used.

关键词： Feedback Application software Computational modeling Telecommunication computing Machine learning Distributed computing Financial advantage program Information technology backpropagation algorithms Performance evaluation

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Stable dynamic backpropagation using constrained learning rate algorithm

Stable dynamic backpropagation using constrained learning ra...

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International Joint Conference on Neural Networks (IJCNN)

作者： Liang Jin M.M. Gupta P.N. Nikiforuk Intelligent Systems Research Laboratory College of Engineering University of Saskatchewan Saskatoon SAS Canada

An equilibrium point learning problem in discrete-time dynamic neural networks is studied in this paper using stable dynamic propagation with constrained learning rate algorithm. The new learning scheme provides an adaptive updating process of the synaptic weights of the network, so that the target pattern is stored at a stable equilibrium point. The applicability of the approach presented is illustrated through a binary pattern storage example.

关键词： backpropagation algorithms Nonlinear dynamical systems Neurons Recurrent neural networks Jacobian matrices Neural networks Stability Iterative algorithms Nonlinear equations Intelligent systems

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backpropagation learning algorithm for nondestructive testing by thermal imager [aerospace materials]

Backpropagation learning algorithm for nondestructive testin...

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International Joint Conference on Neural Networks (IJCNN)

作者： F. Kojima H. Kawaguchi Mechanical Engineering Department Osaka Institute of Technology Osaka Japan

An artificial neural network is applied to nondestructive inspections using thermal imager. The use of an artificial neural network is presented for classifying test data as corresponding to bonded and disbonded regions in sample materials. The backpropagation learning for a multi-layer feedforward neural network is applied to this classification. The trust region method is adopted to the backpropagation learning problem. Results of numerical tests are summarized.

关键词： backpropagation algorithms Nondestructive testing Aerospace materials Artificial neural networks Inspection Aerospace testing Materials testing Bonding Neural networks Multi-layer neural network

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A new acceleration technique for the backpropagation algorithm

A new acceleration technique for the backpropagation algorit...

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International Conference on Neural Networks

作者： X. Yu N.K. Loh W.C. Miller Department of Electrical Engineering University of Windsor Windsor ONT Canada

An adaptive momentum algorithm which can update the momentum coefficient automatically in every iteration step is presented. The basic idea comes from the optimal gradient method. It is very difficult to obtain the optimal gradient vector by analytical methods, but it can be proven that the optimal gradient vectors in two successive iteration steps are orthogonal. Based on this property, one can use the Gram-Schmidt orthogonalization method to ensure the orthogonality of the successive gradient vectors. The result of this process is equivalent to adding a momentum term to the standard backpropagation algorithm. The momentum coefficient is updated automatically in every iteration. Numerical simulations show that the adaptive momentum algorithm can eliminate possible divergent oscillations during the initial training, and can also accelerate the learning process and result in a lower error when the final convergence is reached.< >

关键词： Acceleration backpropagation algorithms Gradient methods Multi-layer neural network Neural networks Feedforward neural networks Numerical simulation Read only memory Convergence of numerical methods Filters

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Reducing the number of multiplies in backpropagation

Reducing the number of multiplies in backpropagation

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International Joint Conference on Neural Networks (IJCNN)

作者： K. Boonyanit A.M. Peterson LSI Logic Corporation Milpitas CA USA STAR Lab Department of Electrical Engineering University of Stanford Stanford CA USA

There have been many algorithms to speed up the learning time of backpropagation. However, most of them do not take into consideration the amount of hardware required to implement the algorithm. Without suitable hardware implementation, the real promise of neural network applications will be difficult to achieve. Since multiply dominates computation and is expensive in hardware, this paper proposes a method to reduce the number of multiplies in the backward path of backpropagation algorithm by setting some neuron errors to zero. It proves the convergence theorem by the general Robbins-Monro process, a stochastic approximation process.< >

关键词： Neurons backpropagation algorithms Convergence Large scale integration Logic Neural network hardware Neural networks Stochastic processes Data flow computing Noise reduction

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Performance Evaluation for Training a Distributed backpropagation Implementation

Performance Evaluation for Training a Distributed BackPropag...

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Sorin Babii Department of Computer and Software Engineering Politehnica University of Timişoara Timisoara Romania

This paper presents the results of some experiments in parallelizing the training phase of a feed-forward, artificial neural network. More specifically, we develop and analyze a parallelization strategy of the widely used neural net learning algorithm called back-propagation. We describe an approach for parallelizing the back- propagation algorithm. We implemented these algorithms on several LANs, permitting us to evaluate and analyze their performances based on the results of actual runs. We were interested on the qualitative aspect of the analysis, in order to achieve a fair understanding of the factors determining the behavior of this parallel algorithms. We were interested in discovering and dealing with some of the specific circumstances that have to be considered when a parallelized neural net learning algorithm is to be implemented on a set of workstations in a LAN. Part of our purpose is to investigate whether it is possible to exploit the computational resources of such a set of workstations.

关键词： Algorithm design and analysis Artificial neural networks backpropagation algorithms Workstations Feedforward systems Performance analysis Performance evaluation Parallel algorithms Neural networks Local area networks

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An automatic adjustment method of backpropagation learning parameters, using fuzzy inference

An automatic adjustment method of backpropagation learning p...

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International Symposium on Multiple-Valued Logic

作者： F. Ueno T. Inoue B.-u.-H. Baloch T. Yamamoto Department of Electrical Engineering and Computer Science Kumamoto University Kumamoto Japan Kure Works Babcock-Hitachi K.K. Hiroshima Japan

Fuzzy inference is introduced into a conventional backpropagation learning algorithm for neural networks. This procedure repeatedly adjusts the learning parameters and leads the system to convergence at the earliest possible time. The technique is appropriate in the sense that optimum learning parameters are being applied in every learning cycle automatically, whereas conventional backpropagation does not contain any well-defined rule regarding the proper selection of learning parameters.< >

关键词： backpropagation algorithms Neurons Multi-layer neural network Computer errors Neural networks Computer science Inference algorithms Temperature Computer simulation Mathematics

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Content-based SMS spam filtering based on the Scaled Conjugate Gradient backpropagation algorithm

Content-based SMS spam filtering based on the Scaled Conjuga...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Waddah Waheeb Rozaida Ghazali Mustafa Mat Deris Computer Science Department Hodeidah University Hodeidah Alduraihimi Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Universiti Tun Hussein Onn Malaysia Batu Pahat Johor MY

ISBN: (纸本)9781467376839

Content-based filtering is one of the most preferred methods to combat Short Message Service (SMS) spam. Memory usage and classification time are essential in SMS spam filtering, especially when working with limited resources. Therefore, suitable feature selection metric and proper filtering technique should be used. In this paper, we investigate how a learnt Artificial Neural Network with the Scaled Conjugate Gradient method (ANN-SCG) is suitable for content-based SMS spam filtering using a small size of features selected by Gini Index (GI) metric. The performance of ANN-SCG is evaluated in terms of true positive rate against false positive rate, Matthews Correlation Coefficient (MCC) and classification time. The evaluation results show the ability of ANN-SCG to filter SMS spam successfully with only one hundred features and a short classification time around to six microseconds. Thus, memory size and filtering time are reduced. An additional testing using unseen SMS messages is done to validate ANN-SCG with the one hundred features. The result again proves the efficiency of ANN-SCG with the one hundred features for SMS spam filtering with accuracy equal to 99.1%.

关键词： Measurement Feature extraction Training Indexes Artificial neural networks backpropagation algorithms Correlation

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Genetic algorithm based pattern allocation schemes for training set parallelism in backpropagation neural networks

Genetic algorithm based pattern allocation schemes for train...

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IEEE International Conference on Evolutionary Computation

作者： Shou King Foo P. Saratchandran N. Sundararajan School of Electrical & Electronic Engineering Nanyang Technological University Singapore

Training set parallelization is an efficient method to optimize the training procedure performance of the backpropagation neural network algorithm. In training set parallelism, the training patterns are distributed 'optimally' among a heterogeneous array of processors, optimality criterion obtain the minimum training time per epoch. Earlier studies on heterogeneous transputers connected in a pipeline-ring topology have indicated that the above optimization problem results in a mixed integer programming problem and results in large computation time to find the optimal pattern allocations. In this paper, a genetic algorithm is used as an optimization tool to find the optimal allocation of patterns. The approach is illustrated using two benchmark problems, the 256-8-256 Encoder and NETTALK problems. Results indicate that when 'a priori' information is not used, the computation time needed by the genetic algorithm is comparable to that obtained by mixed integer programming. However, when 'a priori' information is used, the genetic algorithm results in significant reduction in computation time for finding the optimal solution.

关键词： Genetic algorithms Parallel processing backpropagation algorithms Linear programming Neural networks Intelligent networks Optimization methods Network topology Multi-layer neural network Analytical models

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