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检索条件"主题词=Backpropagation Algorithms"

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Digital satellite channel identification using neural networks: analytic models

Digital satellite channel identification using neural networ...

引用

IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： N.J. Bershad M. Ibnkhala F. Castanie Department of Electrical and Computer Engineering University of California Irvine CA USA National Polytechnics Institute of Toulouse ENSEEIHT GAPSE Toulouse France

This paper studies the statistical transient and convergence behavior of a neural network structure (filter-nonlinearity-filter) that adapts its parameters using a modified version of the backpropagation algorithm. A previous paper has used this structure to model and identify nonlinear satellite channels with memory. This paper derives simplified analytical models which qualitatively predict the algorithm behavior observed previously.

关键词： Satellites Neural networks Nonlinear filters backpropagation algorithms Signal processing algorithms Transversal filters Adaptive filters Delay effects Predictive models Convergence

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A new IIR-MLP learning algorithm for on-line signal processing

A new IIR-MLP learning algorithm for on-line signal processi...

引用

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

作者： P. Campolucci S. Fiori A. Uncini F. Piazza Dipartimento di Elettronica e Automatica Università di Ancona Ancona Italy

We propose a new learning algorithm for locally recurrent neural networks, called truncated recursive backpropagation which can be easily implemented on-line with good performance. Moreover it generalises the algorithm proposed by Waibel et al. (1989) for TDNN, and includes the Back and Tsoi (1991) algorithm as well as BPS and standard on-line backpropagation as particular cases. The proposed algorithm has a memory and computational complexity that can be adjusted by a careful choice of two parameters h and h' and so it is more flexible than a previous algorithm proposed by us. Although for the sake of brevity we present the new algorithm only for IIR-MLP networks, it can be applied also to any locally recurrent neural network. Some computer simulations of dynamical system identification tests, reported in literature, are also presented to assess the performance of the proposed algorithm applied to the IIR-MLP.

关键词： Signal processing algorithms Recurrent neural networks backpropagation algorithms Digital signal processing Signal processing Computer simulation System identification System testing Finite impulse response filter Neural networks

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Comparing parameterless learning rate adaptation methods

Comparing parameterless learning rate adaptation methods

引用

International Conference on Neural Networks

作者： E. Fiesler M. Moreira P.O.C. Torrance CA USA IDIAP Martigny Switzerland

Since the popularization of the backpropagation learning rule for training multilayer neural networks, many improvements and extensions of it have been proposed. Adaptive learning rate techniques are certainly among the most well-known of such improvements, promising a significant increase in learning speed and, in a case when no new tunable parameters are introduced, eliminating the trial-and-error process of finding a suitable learning rate. Hence, in order to compare the most promising of these, five methods without tunable parameters have been selected. Both the online and batch versions of standard backpropagation are also integrated into the study as points of reference. However, in order to compare the convergence speed of different learning rules, a better complexity measure is needed than the commonly used 'number of training iterations'. Hence, a refined complexity measure is introduced here and used in the comparison of the seven chosen methods.

关键词： Multi-layer neural network Neurons Neural networks Convergence Shape Velocity measurement backpropagation algorithms Multidimensional systems Proposals Adaptive scheduling

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Memory based processor array for artificial neural networks

Memory based processor array for artificial neural networks

引用

International Conference on Neural Networks

作者： Youngsik Kim Mi-Jung Noh Tack-Don Han Shin-Dug Kim Sung-Bong Yang Parallel Processing System Laboratory Department of Computer Science Yonsei University Seoul South Korea

ISBN: (纸本)0780341228

In this paper an effective memory-processor integrated architecture, called memory based processor array for artificial neural networks (MPAA), is proposed. The MPAA can be easily integrated into any host system via memory interface. Specifically, the MPAA system provides an efficient mechanism for its local memory accesses allowed by the row basis and the column basis using the hybrid row and column decoding, which is suitable for the computation model of ANNs such as the accessing and alignment patterns given for matrix-by-vector operations. Mapping algorithms to implement the multilayer perceptron with backpropagation learning on the MPAA system are also provided. The proposed algorithms support both neuron and layer level parallelisms which allow the MPAA system to operate the learning phase as well as the recall phase in the pipelined fashion. Performance evaluation is provided by detailed comparison in terms of two metrics such as the cost and the number of computation steps.

关键词： Artificial neural networks Computer architecture Parallel processing backpropagation algorithms Neurons Decoding Computational modeling Memory architecture Bandwidth Read-write memory

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Exploring the effects of Lamarckian and Baldwinian learning in evolving recurrent neural networks

Exploring the effects of Lamarckian and Baldwinian learning ...

引用

IEEE International Conference on Evolutionary Computation

作者： K.W.C. Ku M.W. Mak Department of Electronic Engineering Hong Kong Polytechnic University Hung Hom Hong Kong China

A drawback of using genetic algorithms (GAs) to train recurrent neural networks is that it takes a large number of generations to evolve the networks into an optimal solution. In order to reduce the number of generations taken, the Lamarckian learning mechanism and the Baldwinian learning mechanism are embedded into a cellular GA. This paper investigates the effects of these two learning mechanisms on the convergence performance of the cellular GA. The criteria that make learning useful to GAs are also discussed. The results show that the Lamarckian mechanism is able to assist the cellular GA, while the Baldwinian mechanism fails to do so. In addition to reducing the number of generations taken, we have found that it is also possible to reduce the time taken by embedding learning into the cellular GA in an appropriate manner.

关键词： Intelligent networks Recurrent neural networks Biological cells Learning systems Genetic algorithms Neural networks Stochastic processes Genetic mutations backpropagation algorithms Cellular networks

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Dynamically pruning output weights in an expanding multilayer perceptron neural network

Dynamically pruning output weights in an expanding multilaye...

引用

International Conference on Digital Signal Processing (DSP)

作者： H. Amin K.M. Curtis B.R. Hayes Gill Parallel Processing Specialist Group Department of Electrical & Electronic Engineering University of Nottingham Nottingham UK University of Nottingham Nottingham Nottinghamshire GB

The network size for a multilayer perceptron neural network is often chosen arbitrarily for different applications, and the optimum size of the network is determined by a long process of trial and error. This paper presents a backpropagation algorithm. For a multilayer perceptron (MLP) neural network, that dynamically determines the optimum number of hidden nodes and applies a new pruning technique on output weights. A 29% reduction in the total number of output weights was observed for a handwritten character recognition problem using the new pruning algorithm.

关键词： Multi-layer neural network Intelligent networks Neural networks backpropagation algorithms Multilayer perceptrons Neurons Character recognition Parallel processing Monitoring Mean square error methods

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Wavelet networks as an alternative to neural networks

Wavelet networks as an alternative to neural networks

引用

International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： I. Ciuca J.A. Ware Research Institute for Informatics Bucharest Romania Glamorgan University Pontypridd UK

The paper presents an alternative to the use of feedfoward neural networks as universal approximators. The alternative is based on the wavelet approximation theory of nonlinear functions. An algorithm from the evolutionary computation class is presented for wavelet network learning which as an optional facility, incorporates the capability for removing irrelevant features from input data in classification applications. The results of a dynamic process forecasting application are also presented.

关键词： Neural networks Feedforward neural networks Feedforward systems backpropagation algorithms Principal component analysis Evolutionary computation Genetic programming Informatics Approximation methods Genetic algorithms

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Optimization of trading systems and portfolios

Optimization of trading systems and portfolios

引用

Proceedings of the IEEE/IAFE Computational Intelligence for Financial Engineering (CIFEr)

作者： J. Moody Lizhong Wu CSE Department Oregon Graduate Institute Portland OR USA

We propose to train trading systems and portfolios by optimizing objective functions that directly measure trading and investment performance. Rather than basing a trading system on forecasts or training via a supervised learning algorithm using labelled trading data, we train our systems using recurrent reinforcement learning algorithms. The objective functions that we consider as evaluation functions for reinforcement learning are profit or wealth, economic utility, the Sharpe ratio, and our proposed Differential Sharpe Ratio. The trading and portfolio management systems require prior decisions as input in order to properly take into account the effects of transactions costs, market impact, and taxes. This temporal dependence on system state requires the use of reinforcement versions of standard recurrent learning algorithms. We present empirical results in controlled experiments that demonstrate the efficacy of some of our methods. We find that maximizing the differential Sharpe ratio yields more consistent results than maximizing profits, and that both methods outperform a trading system based on forecasts that minimize MSE.

关键词： Portfolios Economic forecasting Costs Investments Supervised learning Finance Labeling backpropagation algorithms Management training Optimization methods

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A comparative study of fully and partially recurrent networks

A comparative study of fully and partially recurrent network...

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International Conference on Neural Networks

作者： J. Ludik W. Prins K. Meert T. Catfolis Department of Computer Science University of Stellenbosch Stellenbosch South Africa Department of Chemical Engineering Expert Systems Applications Development Group Heverlee Belgium

A number of fully and partially recurrent networks have been proposed to deal with temporally extended tasks. However, it is not yet clear which algorithms and network architectures are best suited to certain kinds of problems. In this paper we report on experimental investigations of a quantitative nature, which address this particular need by comparing fully recurrent networks using learning algorithms such as backpropagation-through-time (BPTT), batch BPTT Quickprop-through-time, and real-time recurrent learning with Elman and Jordan partially recurrent networks on four benchmark problems: detection of three consecutive zeros, nonlinear plant identification, Turing machine emulation, and real-world distillation column modelling.

关键词： backpropagation algorithms Emulation History Computer science Africa Expert systems Application software Chemical engineering Distillation equipment Recurrent neural networks

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Performance of adaptive Bayesian equalizers in outdoor environments

Performance of adaptive Bayesian equalizers in outdoor envir...

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IEEE Conference on Vehicular Technology (VTC)

作者： J.L. Valenzuela F. Casadevall Departament de Teoria del Senyal i Comunicacions E.T.S.E.B. Universitat Politecnica de Catalunya Barcelona Spain

Outdoor communications are affected by multipath propagation that imposes an upper limit on the system data rate and restricts possible applications. In order to overcome the degrading effect introduced by the channel, conventional equalizers implemented with digital filters have been traditionally used. A new approach based on neural networks is considered. In particular, the behavior of the adaptive Bayesian equalizer implemented by means of radial basis functions applied to the channel equalization of radio outdoor environments has been analyzed. The method used to train the equalizer coefficients is based on a channel response estimation. We compare the results obtained with three channel estimation methods: the least sum of square errors (LSSE) channel estimation algorithm, recursive least square (RLS) algorithm employed only to obtain one channel estimation and, finally, the RLS algorithm used to estimate the channel every decided symbol for the whole frame.

关键词： Bayesian methods backpropagation algorithms Decision feedback equalizers Maximum likelihood estimation Channel estimation Clustering algorithms Delay Power system modeling Degradation Intersymbol interference

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