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检索条件"主题词=Backpropagation Algorithms"

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A robust extended Elman backpropagation algorithm

A robust extended Elman backpropagation algorithm

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International Joint Conference on Neural Networks (IJCNN)

作者： Qing Song Yeng Chai Soh Lei Zhao School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Elman networks (ENs) can be viewed as a feedforward (FF) neural network with an additional set of inputs from the context layer input (feedback from the hidden layer). Therefore, a standard on-line (real time) backpropagation (BP) algorithm, instead of the off-line backpropagation through time (BPTT) algorithm, can be applied for the training of ENs, which is usually called Elman backpropagation (EBP) for discrete time sequence prediction applications. However, the standard BP training algorithm is not the most suitable one for ENs. Using a small learning rate may help improve the training of ENs, but it can result in very slow convergence speed and poor generalization performance, while a large learning rate may lead to unstable training in terms of weight divergence. Therefore, an optimal trade-off between ENs training speed and weight convergence with good generalization capability is desired. In this paper, a robust extended Elman backpropagation (eEBP) training algorithm of ENs with a nonlinear adaptive dead zone scheme is developed based on a novel training concept. The optimized adaptive learning rate with the adaptive dead zone maximizes the training speed of the ENs for each weight updating step while generalization performance of the eEBP training is improved. Computer simulations are carried out to show the improved performance of eEBP for discrete-time sequence prediction.

关键词： Robustness backpropagation algorithms Neural networks Humans Optical computing Testing Elbow Image motion analysis Legged locomotion Image segmentation

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On Combining backpropagation with Boosting

On Combining Backpropagation with Boosting

引用

International Joint Conference on Neural Networks (IJCNN)

作者： M. Korytkowski L. Rutkowski R. Scherer Department of Computer Engineering Czestochowa University of Technology Czestochowa Poland Department of Artificial Intelligence Academy of Humanities and Economics Lodz Poland

Boosting is a method for learning combined classifiers. In a boosting ensemble of classifiers trained by the backpropagation algorithm, the learning rate takes much smaller value comparing with the backpropagation applied alone. We propose a method which overcomes the above drawback and test it on neuro-fuzzy systems constituting a classifier ensemble using some well known benchmarks.

关键词： Boosting backpropagation algorithms System testing Fuzzy neural networks Benchmark testing Robustness Artificial intelligence Learning Equations

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A single-layer systolic architecture for backpropagation learning

A single-layer systolic architecture for backpropagation lea...

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International Conference on Neural Networks

作者： J.F. Myoupo D. Seme Laboratoire de Recherche en Informutique d&3x2019 Amiens LaRIA CURI Université de Picardie Jules Verne Amiens France

A systolic array algorithm for backpropagation is suggested. The speed and single layer architecture appear superior to that of Kung-Hwang algorithm (1988, 1989).

关键词： backpropagation algorithms Systolic arrays Computer architecture Iterative algorithms Artificial neural networks Biological system modeling Biology computing Neural networks Algorithm design and analysis Multi-layer neural network

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An efficient learning algorithm for the backpropagation artificial neural network

An efficient learning algorithm for the backpropagation arti...

引用

IEEE Southeastcon

作者： A.L. Paul P.C. Byrne Univ. of South Alabama Mobile AL USA

Two conditions for reducing the number of learning iterations in backpropagation artificial neural networks are introduced. The first condition is to scale the target output so that it falls within a small range (+or-0.1) of the point at which the slope of the nonlinear activation function of the output node is maximum. This point is 0.5 for the sigmoid function. The second condition is to learn the input patterns selectively, not sequentially, until the error is reduced below the desired limit. Introducing the techniques does not affect the memory retention or generalization capabilities of such networks. The application of these concepts to the classical XOR learning algorithm problem resulted in a reduction in the number of learning iterations by a factor of seven over the results published by D.E. Rumelhart et al. (Parallel Distributed Processing, vol.1, chap.8, Cambridge, MIT Press).< >

关键词： backpropagation algorithms Artificial neural networks Equations Computer networks Algorithm design and analysis

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Complex backpropagation neural network using elementary transcendental activation functions

Complex backpropagation neural network using elementary tran...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： T. Kim T. Adali Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA MITRE Corporation McLean VA USA

Designing a neural network (NN) for processing complex signals is a challenging task due to the lack of bounded and differentiable nonlinear activation functions in the entire complex domain C. To avoid this difficulty, 'splitting', i.e., using uncoupled real sigmoidal functions for the real and imaginary components has been the traditional approach, and a number of fully complex activation functions introduced can only correct for magnitude distortion but can not handle phase distortion. We have previously introduced a fully complex NN that uses a hyperbolic tangent function defined in the entire complex domain and showed that for most practical signal processing problems, it is sufficient to have an activation function that is bounded and differentiable almost everywhere in the complex domain. In this paper, the fully complex NN design is extended to employ other complex activation functions of the hyperbolic, circular, and their inverse function family. They are shown to successfully restore the nonlinear amplitude and phase distortions of non-constant modulus modulated signals.

关键词： Neural networks Signal processing Phase distortion backpropagation algorithms Nonlinear distortion Signal restoration Phase modulation Least squares approximation Quadrature phase shift keying Equations

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A normalised backpropagation learning algorithm for multilayer feed-forward neural adaptive filters

A normalised backpropagation learning algorithm for multilay...

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IEEE Workshop on Neural Networks for Signal Processing

作者： A.I. Hanna D.P. Mandic M. Razaz School of Information Systems University of East Anglia Norwich UK

Analysis of a normalised backpropagation (NBP) algorithm employed in feed-forward multilayer nonlinear adaptive filters trained by backpropagation is provided. It is first shown that a degree of freedom in training of a nonlinear adaptive filter can be removed according to the relationship between the gain of the activation function, learning rate and weight matrix. The derivation of the NBP algorithm for a multilayer feed-forward neural adaptive filter is then provided based upon the minimisation of the instantaneous output error of the filter. Simulation results show that the NBP algorithm converges faster than a standard backpropagation algorithm and achieves better prediction gain when applied to nonlinear and non-stationary signals.

关键词： backpropagation algorithms Nonhomogeneous media Feedforward systems Adaptive filters Character generation

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A Comparison of PSO and backpropagation Combined with LS and RLS in Identification Using Fuzzy Neural Networks

A Comparison of PSO and Backpropagation Combined with LS and...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Niusha Shafiabady M. Teshnehlab M. Aliyari Shooredeh Department of Mechatronics Engineering Azad University Science and Research center Tehran Iran K.N. Toosi university of technology Tehran Iran

In this article using a population-based method, particle swarm optimization in training the standard deviation and centers of radial basis function fuzzy neural networks is put into practice and the results are compared with training the same networks' standard deviation and centers using backpropagation. We have applied Least Square and Recursive Least Square in training the weights of this fuzzy neural networks . There are four sets of data used to examine and prove that according to the convergence speed and the identification error particle swarm optimization works better and as its complexity is much less, it can be suggested as a good solution for training the parameters.

关键词： Resonance light scattering Fuzzy neural networks Neural networks Particle swarm optimization Least squares methods backpropagation algorithms Neurons Intelligent networks Mechatronics Convergence

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Benchmarking of the CM-5 and the Cray machines with a very large backpropagation neural network

Benchmarking of the CM-5 and the Cray machines with a very l...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Xiao Liu G.L. Wilcox Department of Pharmacology and Minnesota Supercomputer Institute University of Minnesota MN USA

In this paper, we present a new, efficient implementation of the backpropagation algorithm (BP) on the CM-5 by fully taking advantage of its Control Network to avoid explicit message-passing. The nodes in the input and output layers are evenly distributed to all processors: all nodes in the hidden layer(s) are replicated in each processor, and all weights are distributed to all processors corresponding to the nodes. We have implemented this algorithm on the CM-5 in the MIMD mode using the C programming language. For a case study of protein tertiary structure prediction, we obtained performance of 76 million weight updates per second (WUPS) with the machine partitioned for 512 processors without vector units. Experiments using different sized partitions indicated an almost linear relationship between the computation time and the number of processors, indicating good parallelization. We have also implemented the backpropagation algorithm on the Cray machines using the C programming language. The Cray-2 implementation yields performance of 10 million WUPS; the Cray X-MP EA implementation yields 18 million WUPS; and the Cray Y-MP M92 implementation yields 40 million WUPS.< >

关键词： Neural networks Proteins Supercomputers backpropagation algorithms Partitioning algorithms Testing Amino acids Biological neural networks Computer languages Concurrent computing

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SoC-Based Implementation of the backpropagation Algorithm for MLP

SoC-Based Implementation of the Backpropagation Algorithm fo...

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International Conference on Hybrid Intelligent Systems (HIS)

作者： R.J. Aliaga R. Gadea R.J. Colom J.M. Monzó Ch.W. Lerche J.D. Martínez A. Sebastiá F. Mateo Institute for the Implementation of Advanced Information and Communications Technologies Polytechnic University of Valencia Spain

The backpropagation algorithm used for the training of multilayer perceptrons (MLPs) has a high degree of parallelism and is therefore well-suited for hardware implementation on an ASIC or FPGA. However, most implementations are lacking in generality of application, either by limiting the range of trainable network topologies or by resorting to fixed-point arithmetic to increase processing speed. We propose a parallel backpropagation implementation on a multiprocessor system-on-chip (SoC) with a large number of independent floating-point processing units, controlled by software running on embedded processors in order to allow flexibility in the selection of the network topology to be trained. It is shown that the speed of such a system is limited by the communication overhead between processing nodes, especially by the management of training vectors. Preliminary performance results on an Altera DE2-70 development board are given and optimal architecture parameters are selected.

关键词： Training Network topology Neurons Program processors backpropagation algorithms backpropagation Field programmable gate arrays

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Study of cost functions in Three Term backpropagation for classification problems

Study of cost functions in Three Term Backpropagation for cl...

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World Congress on Nature & Biologically Inspired Computing (NaBIC)

作者： Siti Mariyam Shamsuddin Razana Alwee Puspadevi Kuppusamy Maslina Darus Soft Computing Research Group Universiti Teknologi Malaysia Skudai Johor Malaysia School of Mathematical Sciences Universiti Kebangsaan Malaysia Bangi Selangor Darul Ehsan Malaysia

Three term backpropagation (BP) network as proposed by Zweiri in 2003 has outperformed standard two term backpropagation. However, further studies on three term backpropagation in 2007 indicated that this network only surpassed standard BP for small scale datasets but not for medium and large scale datasets. It has also been observed that by using mean square error (MSE) as a cost function in three term BP has some drawbacks, and these include incorrect saturation and tend to trap in local minima, resulting in slow convergence and poor performance. In this study, thorough experiments on implementing various cost functions are executed to probe the effectiveness of Three Term BP network. The cost functions under investigations include mean square error (MSE), Bernoulli function, Modified cost function and Improved cost function. The results reveal that MSE is not an ideal cost function to be used for three term BP. Hence, alternative cost functions need to be considered when using BP network for classification problems.

关键词： Cost function Convergence backpropagation algorithms Electronic mail Large-scale systems Mean square error methods Computer networks Probes Computational efficiency Computational complexity

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