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检索条件"主题词=Backpropagation Algorithms"

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Some enhancements of the constraint based decomposition training architecture

Some enhancements of the constraint based decomposition trai...

引用

International Conference on Neural Networks

作者： S. Draghici Departmentof Computational Science University of Saint Andrews UK

This paper presents three mechanisms: locking detection (LD), redundancy elimination (RE) and generalisation control (GC). Although originated, implemented and tested for the constraint based decomposition (CBD), LD and RE address general drawbacks of constructive algorithms and can be applied to other such algorithms, as well. LD stops the training of a hyperplane if its position is pin-pointed by nearby patterns, thus improving the training speed. RE reduces the number of hyperplanes used in the solution. RE works on-line during the training, thus eliminating the need for a separate pruning stage. Finally, GC addresses the generalisation properties of the solution. It is shown that CBD's generalisation can be controlled by the user through the ordering of the pattern set. The experiments presented show that these mechanisms are effective on various types of problems.

关键词： backpropagation algorithms Neurons Computer architecture Testing Multilayer perceptrons

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Two point training for complex plane transformation

Two point training for complex plane transformation

引用

International Conference on Neural Networks

作者： Jingyan Song Yeung Yam Department of Mechanical and Automation Engineering Chinese University of Hong Kong Sha Tin Hong Kong China

The complex neural network whose weights, threshold values, inputs and outputs signals are all complex variables is studied in this paper. It is shown that using a training set of only two points in the complex plane,... 详细信息

ISBN: (纸本)0780332105

关键词： Neural networks Signal processing algorithms Neurons Multi-layer neural network backpropagation algorithms Automation Signal processing Least squares approximation Algorithm design and analysis Zirconium

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On incorporating finite impulse response neural network with finite difference time domain method for simulating electromagnetic problems

On incorporating finite impulse response neural network with...

引用

Antennas and Propagation Society International Symposium

作者： Chen Wu Minh Nguyen J. Litva Wireless Technique Group Communications Research Laboratory McMaster University Hamilton ONT Canada

The finite difference time domain method (FDTD) is a very powerful numerical method to solve electromagnetic (EM) problems. It is very flexible to simulate the problems which have very complex boundaries. It is well known that FDTD method requires long computation time for solving the resonant or high-Q passive structures. The reason for this is because the algorithm is based on the leap-frog formula. For EM modeling is very important to speed up the simulation. The finite impulse response neural network (FIR NN) is applied as a nonlinear predictor to predict time series signal for speeding up the FDTD simulations. The FIR NN is trained by temporal backpropagation learning algorithm. A waveguide filter is used as an example and simulated by the FDTD method. It demonstrates that a short segment of an FDTD data is used to train the predictor, and the predictor can predict later information very well. The total least square (TLS) method is used as a predictor as well. By comparing the predicted error, it is shown that FIR neural network gives better prediction than that of the TLS.

关键词： Neural networks Finite difference methods Time domain analysis Finite impulse response filter Computational modeling backpropagation algorithms Resonance Predictive models Electromagnetic waveguides Least squares methods

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Performance evaluation of time-delay fuzzy neural networks for isolated word recognition

Performance evaluation of time-delay fuzzy neural networks f...

引用

International Symposium on Neuro-Fuzzy Systems, AT

作者： K. Oweiss O. Abdel Alim Dept. of Electron. & Comput. Eng. Arab Acad. for Sci. & Technol. Alexandria Egypt

A novel structure of fuzzy neural network (FNN) for the recognition of isolated Arabic words is suggested. The performance is evaluated by varying the network topology among several experiments to select the optimum structure for our task. A time delay arrangement is incorporated in the training phase to enable the network to discover useful acoustic-phonetic features without being blurred by shifts in the input. The input speech is processed to obtain a set of linear predictive (LP) derived cepstral coefficients. The input vector to the FNN consists of membership values to linguistic properties of the speech while the output vector is defined in terms of fuzzy class membership values. Three techniques were used to enhance the backpropagation training algorithm used to train the network in order to reduce training time and speed up convergence. The effectiveness of the suggested model is demonstrated on a speech recognition task consisting of Arabic phonemes extracted from a consonant-vowel-consonant (C-V-C) personnel database.

关键词： Fuzzy neural networks Vectors Network topology Delay effects Speech processing Cepstral analysis backpropagation algorithms Convergence Speech recognition Personnel

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Nonlinear predictive modeling using dynamic non-singleton fuzzy logic systems

Nonlinear predictive modeling using dynamic non-singleton fu...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： G.C. Mouzouris J.M. Mendel Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

We investigate the dynamic versions of fuzzy logic systems (FLSs), and specifically their nonsingleton generalizations (NSFLSs), and derive a dynamic learning algorithm to train the system parameters. The history-sensitive output of the dynamic systems gives them a significant advantage over static systems in modeling processes of unknown order. Since dynamic NSFLSs can be considered to belong to the family of general nonlinear autoregressive moving average (NARMA) models, they are capable of parsimoniously modeling NARMA processes. We study the performance of both dynamic and static FLSs in the predictive modeling of a NARMA process.

关键词： Predictive models Fuzzy logic Nonlinear dynamical systems Uncertainty Output feedback Signal processing Image processing Fuzzy systems backpropagation algorithms Neural networks

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A new fast U-D factorization-based learning algorithm with applications to nonlinear system modeling and identification

A new fast U-D factorization-based learning algorithm with a...

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International Conference on Neural Networks

作者： Youmin Zhang X. Rong Li Department of Electrical Engineering University of New Orleans New Orleans LA USA

ISBN: (纸本)0780332105

A new fast learning algorithm for training multilayer feedforward neural networks by using variable forgetting factor technique and U-D factorization-based fading memory extended Kalman filter is proposed. In comparison with the backpropagation (BP) and extended Kalman filter (EKF) based learning algorithms, the proposed algorithm can provide much more accurate learning results in fewer iterations with fewer hidden nodes as well as improve convergence rate and numerical stability. In, addition, it is less sensitive to the choice of initial weights and initial covariance matrix as well as other setup parameters. Simulation results of nonlinear dynamic system modeling and identification show that the new algorithm proposed here is an effective and efficient learning algorithm for feedforward neural networks.

关键词： backpropagation algorithms Multi-layer neural network Neural networks Feedforward neural networks Fading Convergence of numerical methods Numerical stability Covariance matrix Nonlinear dynamical systems Modeling

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Robust BP theory and algorithms based on several kinds of error estimators

Robust BP theory and algorithms based on several kinds of er...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Xiaofeng Liao Wenquan Mu Juebang Yu National Laboratory of Patfern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)0780329120

The mean-squared error estimator was used by standard BP (backpropagation) algorithms. Therefore these algorithms might get trapped in some local minimum, have slow convergence and be sensitive to initial weight values etc. In this paper, a kind of new robust BP mathematical theory which is based on the Lagrangian multiplier method and several kinds of robust error estimators is investigated in detail. Robust BP algorithms are obtained. Experiments illustrate: our algorithms not only converge fast and are less sensitive to initial weight values, but also can overcome the influence of "outliers". The algorithms are robust for little noise perturbation and gross error.

关键词： Signal processing algorithms backpropagation algorithms Convergence Iterative algorithms Noise robustness Neurons Lagrangian functions Biological neural networks Laboratories Automation

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A neural network training algorithm utilizing multiple sets of linear equations

A neural network training algorithm utilizing multiple sets ...

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Asilomar Conference on Signals, Systems & Computers

作者： Hung-Han Chen M.T. Manry Department of Electrical Engineering University of Texas Arlington Arlington TX USA

A fast algorithm is presented for the training of multilayer perceptron neural networks. In each iteration, there are two passes through the training data. In the first pass, linear equations are solved for the output weights. In the second data pass, linear equations are solved for hidden unit weight changes. Full batching is used in both data passes. An algorithm is described for calculating the learning factor for use with the hidden weights. It is shown that the technique is significantly faster than standard output weight optimization-backpropagation.

关键词： Neural networks Equations backpropagation algorithms Multilayer perceptrons Multi-layer neural network Joining processes Training data Feeds Additives Mean square error methods

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Quasi-optimum detection results using a neural network

Quasi-optimum detection results using a neural network

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International Conference on Neural Networks

作者： D. Andina J.L. Sanz Gonzalez Departam ento de Señales Sistem as y .R wdiocom unicaciones ETSI de Telecom unicacidn Universidad Politecnica de Madrid Madrid Spain

We study some particularities for the application of a neural network to binary detection. Using a modeled input (the classical J. Marcum model for pulsed radar detection), we optimize the design of the network, evaluating its performance by Monte Carlo trials. After comparing the detection curves with the theoretical optimum ones, it is found that the number of pulses integrated for each detection is critical for a quasi-optimum performance of the neural network.

关键词： Detectors Neural networks Radar detection Concurrent computing Hardware backpropagation algorithms Parallel processing Telecommunication standards Electronic mail Design optimization

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Neural network model based on weights and unit-offset control

Neural network model based on weights and unit-offset contro...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Chunhwan Lim Jongan Park Department of Electronic Engineering Chosun University Kwangju South Korea

ISBN: (纸本)0780329120

In this paper, we propose an improved algorithm for a neural network. It consists of multilayer neural networks with control part and address memory part. The control part controls weights between the input layer and the hidden layer, controls unit offsets of the hidden layer, and sends the output data to the address memory part. The address memory part memorizes the output pattern of the hidden layer, compared with the input pattern and sends the learning data to the output layer after learning. Simulation results show that it is easy to control weights and unit-offset. Its convergence speed is fast.

关键词： Neural networks Weight control Multi-layer neural network Error correction Convergence Equations Neurons Electronic mail backpropagation algorithms Learning systems

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