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检索条件"主题词=Backpropagation algorithms"

共 1906 条记录，以下是231-240 订阅

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Functional site prediction on the DNA sequence by artificial neural networks

Functional site prediction on the DNA sequence by artificial...

引用

IEEE International Joint Symposia on Intelligence and Systems

作者： Hatzigeorgiou, A Mache, N Reczko, M European Molecular Biology Lab Heidelberg Germany

ISBN: (纸本)0818677287

A modular system of neural networks is used to identify genes in DNA sequences of eukaryotic organisms. The identification task is decomposed into the detection of distinct signals using separate neural network modules. Such signals are coding regions, splice sites and transcription start regions (cap-site). A focus of the work is the use of Back-percolation, cascade correlation, and Time-Delay neural networks. These give, in this in this particular application, better generalization than the well known backpropagation algorithm. This system achieves a prediction accuracy comparable to the traditionally design ed gene identification packages and is able to produce more accurate protein sequences from the constructed gene structures.

关键词： DNA Sequence Neural network Modular systems backpropagation algorithms Gene Annotation Genes signals

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A biologically inspired connectionist system for natural language processing 7

A biologically inspired connectionist system for natural lan...

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7th Brazilian Symposium on Neural Networks (SBRN 02)

作者： Rosa, JLG Pontificia Univ Catolica Campinas Mestrado Sistemas Computacao Mestrado Informat UniSantos BR-13012970 Campinas SP Brazil

ISBN: (纸本)0769517099

Nowadays artificial neural network models often lack many physiological properties of the nervous cell. Feedforward multilayer perceptron architectures, and even simple recurrent networks, still in vogue, are far from those encountered in cerebral cortex. Current learning algorithms are more oriented to computational performance than to biological credibility. The aim of this paper is to propose an artificial neural network system, called Bio-thetaR, including architecture and algorithm, to take care of a natural language processing problem, the thematic relationship, in a biologically inspired connectionist approach. Instead of feedforward or simple recurrent network, it is presented a bi-directional architecture. Instead of the well-known biologically implausible backpropagation algorithm, a neurophysiologically motivated one is employed to account for linguistic thematic role assignment in natural language sentences. In addition, several features concerning biological plausibility are also included.

关键词： Artificial neural networks backpropagation algorithms Bidirectional control Biological system modeling Biology computing Computer architecture Mathematical model Natural language processing Natural languages Psychology

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On the analysis of sigmoid time parameters for dynamic truncated BPTT algorithm

On the analysis of sigmoid time parameters for dynamic trunc...

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IEEE International Joint Conference on Neural Network

作者： Scesa, V. Henaff, P. Ouezdou, F. B. Namoun, F. Versailles Univ UVSQ CNRS LIRIS Lab 1012 Av Europe F-78140 Velizy Villacoublay France LIRIS UVSQ CNRS Lab F-75700 Paris France BIA Co F-78700 Conflans France

ISBN: (纸本)9780780394902

The purpose of the research addressed in this paper concerns a comparative study of two expressions of the time scale parameter for Continuous Time Recurrent Neural Network (CTRNN): a classical time constant expression, and a sigmoid one. Their influence on the stability, the convergence speed and the generalization ability of a backpropagation Through Time (BPTT) learning algorithm, will be discussed. Firstly, three mathematical conclusions related to the propagation and learning equations are deduced. Then these conclusions are validated on experiments carried out on a real biped robot. Through the identification of the balancing behavior under different robot torso motions, the sigmoid expression will be shown to get the best learning results.

关键词： backpropagation algorithms

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A New BP Algorithm with Adaptive Momentum for FNNs Training

A New BP Algorithm with Adaptive Momentum for FNNs Training

引用

1st WRI Global Congress on Intelligent Systems (GCIS 2009)

作者： Shao, Hongmei Zheng, Gaofeng China Univ Petr Coll Math & Comp Sci Dongying 257061 Peoples R China JANA Sol Inc Minato Ku Tokyo 105 Japan

ISBN: (纸本)9780769535715

In this paper a new back-propagation (BP) algorithm with adaptive momentum is proposed, where the momentum coefficient is adjusted iteratively based on the current descent direction and the weight increment in the last iteration. A convergence result of the algorithm is presented when it is used for training feedforward neural networks (FNNs) with a hidden layer Simulation results have shown that this new algorithm has a distinct superiority in fast convergence and smoothing oscillation over the conventional BP method. Moreover the range for the learning rate has been widened after the inclusion of such an adaptable momentum while maintaining the stability of networks.

关键词： Convergence backpropagation algorithms Iterative algorithms Petroleum Neural networks Feedforward neural networks Smoothing methods Stability Error correction Multi-layer neural network

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Parallelization of Artificial Neural Network Training algorithms: A Financial Forecasting Application

Parallelization of Artificial Neural Network Training Algori...

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IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr)

作者： Casas, C. Augusto St Thomas Aquinas Coll Sparkill NY 10976 USA

ISBN: (纸本)9781467318037

Artificial neural networks (ANN) are widely used to solve series prediction problems such as prices of financial instruments. backpropagation is the most common artificial neural training algorithm. This paper discusses results obtained with the parallelization of the backpropagation algorithm used to train a network that forecasts the S&P500 Index. Training this ANN involves the processing of vast amounts of historical financial data which is time consuming. Financial markets;however, constitute fast paced environments where decisions need to make shortly after new information becomes available. Parallelizing the backpropagation algorithm to run on four processors simultaneously resulted in a reduction of 61% in training time compared to the same algorithm running without parallelization.

关键词： parallelization backpropagation algorithms Artificial neural networks Prices Stock exchanges

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A chemical reactor benchmark for parallel adaptive control using feedforward neural networks 6

A chemical reactor benchmark for parallel adaptive control u...

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6th Brazilian Symposium on Neural Networks

作者： Cajueiro, DO Hemerly, EM IEES IEE ITA BR-12228900 SJC SP Brazil

ISBN: (纸本)0769508561

This paper applies a parallel scheme for adaptive control that uses only one neural network to a CSTR (Continuous Stirred Tank Reactor). Convergence of the identification error is investigated by Lyapunov's second method The training process of the neural network is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm.

关键词： Adaptive control backpropagation algorithms Chemical reactors Continuous-stirred tank reactor Feedforward neural networks Network topology Neural networks Recurrent neural networks Testing Uncertainty

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DGPS/INS integration using neural network methodology 12

DGPS/INS integration using neural network methodology

引用

12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2000)

作者： Ibrahim, F Tascillo, A Al-Holou, N Univ Detroit Mercy Detroit MI 48221 USA

ISBN: (纸本)076950910X

This paper presents an INS/DGPS land vehicle navigation system using a neural network methodology. The network setup is developed based on a mathematical mode] to avoid excessive training. The proposed method uses a KF-based backpropagation training rule, which achieves the optimal training criterion. The North and East travel distances are used as desired targets to train the two decoupled neural networks. The proposed method is suitable for INS and DGPS systems sampled at different rates. In addition, an online stochastic modeling method for the desired target is developed. This method facilitates the use of the extended Kalman Filter trained backpropagation Neural Network approach whenever the desired target statistics are not available, or not reliable. The experimental results demonstrate the suitability of this method in developing an INS/DGPS land vehicle navigation method.

关键词： Artificial neural networks backpropagation algorithms Convergence Covariance matrix Equations Global Positioning System Land vehicles Navigation Neural networks State estimation

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A stochastic backpropagation algorithm for training neural networks

A stochastic backpropagation algorithm for training neural n...

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International Conference on Information, Communications and Signal Processing

作者： Y.Q. Chen T. Yin H.A. Babri School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

The popularly used backpropagation algorithm (BP) for training multilayered neural networks is generally slow and prone to getting stuck in local minima. A novel method to improve the performance of the BP by randomising the cost function is proposed. The method is effective in helping the BP algorithm to escape from local minima and therefore improve the convergence and generalization. This is demonstrated on a non-convex pattern recognition problem.

关键词： Stochastic processes backpropagation algorithms Neural networks Cost function Multi-layer neural network Convergence Pattern recognition Feedforward neural networks Entropy Jacobian matrices

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Adaptive Moment Estimation To Minimize Square Error In backpropagation Algorithm

Adaptive Moment Estimation To Minimize Square Error In Backp...

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International Conference of Computer Science and Information Technology

作者： Roy Nuary Singarimbun Erna Budhiarti Nababan Opim Salim Sitompul Graduate School of Computer Science and Department of Information Technology Universitas Sumatera Utara Medan Indonesia

backpropagation (BP) has long training time and slow convergence, i.e. in minimizing Mean Square Error (MSE). Adaptive Moment Estimation (ADAM) was employed to speed up training and to obtain rate of BP. Result shown ... 详细信息

ISBN: (数字)9781728154237

ISBN: (纸本)9781728154244

关键词： Artificial Neural Network (ANN) Gradient Descent backpropagation MSE minimization Adaptive Moment Estimation (ADAM) back propagation backpropagation algorithms quadratic errors Mean square error Moment of force Self tuning Training

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The autoregressive backpropagation algorithm

The autoregressive backpropagation algorithm

引用

International Joint Conference on Neural Networks (IJCNN)

作者： R.R. Leighton B.C. Conrath MITRE Corporation McLean VA USA

Describes an extension to error backpropagation that allows the nodes in a neural network to encode state information in an autoregressive 'memory'. This neural model gives such networks the ability to learn to recognize sequences and context-sensitive patterns. Building upon the work of A. Wieland (1990) concerning nodes with a single feedback connection, the authors generalize the method to n feedback connections and address stability issues. The learning algorithm is derived, and a few applications are presented.< >

关键词： backpropagation algorithms Neurofeedback Neurons Stability Output feedback Delay Digital filters Difference equations Neural networks Context modeling

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