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检索条件"主题词=Backpropagation Algorithms"

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A learning strategy for multilayer neural network using discretized Sigmoidal function

A learning strategy for multilayer neural network using disc...

引用

International Conference on Neural Networks

作者： S. Anna Durai P.V. Siva Prasad A. Balasubramaniam V. Ganapathy Anna University Chennai Chennai Tamil Nadu IN Sch. of Comput. Sci. & Eng. Anna Univ. Madras India

In this paper a new approach for training the multilayer neural network (MNN), is proposed based on the scheme of discretization of the sigmoidal threshold activation function at regular intervals. The discretized values are stored in the form of a look up table (LUT). The weights are updated on a layer by layer basis using the values in the LUT. The proposed algorithm is applied to pattern classification problems and tested experimentally. It is found that the proposed algorithm takes lesser training time than the conventional backpropagation algorithm with continuous sigmoidal function. The proposed algorithm is more suitable for hardware implementation.

关键词： Multi-layer neural network Neural networks Table lookup Computer science Signal processing algorithms backpropagation algorithms Error correction Equations Neurons Testing

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Traditional and neural networks algorithms: applications to the inspection of marble slab

Traditional and neural networks algorithms: applications to ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： V.G. Hernandez P.C. Perez L.G.G. Perez L.M.T. Balibrea H. Puyosa Pina Grupo de Visión y Robótica Departamento de Automática Electricidad y EIectronica IndustrialEscuela Técnica Superior de Ingenieros Industriales Universidad de Murcia Spain Grupo de Visiony Robotica Departamento de Automática Electricidad y EIectronica IndustrialEscuela TCcnica Superior de Ingenieros Industriales Universidad de Murcia Spain Grupo de Vision y Robotica Murcia Univ. Cartagena Spain

This article describes the traditional algorithms of deterministic and stochastic cluster analysis and classification (a priori and with learning), by comparing the results obtained using the classifiers based on neural networking techniques in supervised learning (multilayer perceptron with backpropagation). The selected samples, chosen to check the algorithms have been marble slabs of "Sierra de la Puerta" type.

关键词： Neural networks Inspection Slabs Color Surface texture Clustering algorithms backpropagation algorithms Service robots Robot vision systems Statistics

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An artificial neural network to linearize a G (tungsten vs. tungsten 26% rhenium) thermocouple characteristic in the range of zero to 2000/spl deg/C

An artificial neural network to linearize a G (tungsten vs. ...

引用

IEEE International Symposium on Industrial Electronics (ISIE)

作者： M. Attari F. Boudjema M. Heniche Institute of Electronics Department of Instrumentation University of Sciences and Technology Houari-Boumediene Algiers Algeria Inst. of Electron. Houri Boumediene Univ. of Sci. & Technol. Algiers Algeria Department of Electrical Engineering Ecole Nationale Polytechnique Algiers Algeria

An alternative way to correct sensors in order to exhibit good linearity is proposed. This paper describes the design and behavior of an artificial neural network (ANN) with two hidden layers of twelve neurons each for linearizing a static characteristic of a G thermocouple ranged from zero to 2000/spl deg/C. The use of interpolation methods to perform such corrections is also discussed. In the training phase of the ANN, backpropagation and random optimization algorithms have jointly been used to adjust the weights of the network for a desired final error. After training the ANN, it is then used as a neural linearizer to generate the primary variable (temperature) from the thermocouple's output voltage. The accuracy of this method is compared with those obtained by interpolation.

关键词： Tungsten Artificial neural networks Temperature sensors Interpolation Plasma temperature Thermal sensors Read only memory Instruments Sensor phenomena and characterization backpropagation algorithms

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CONTROLAB: an integrated system for intelligent control of a robot arm

CONTROLAB: an integrated system for intelligent control of a...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： E.P.L. Aude F.A.B. Silva E.P. Lopes M.F. Martins H. Serdeira NCE-UFRJ Rio de Janeiro Rio de Janeiro Brazil Instituto de Matemática Rio de Janeiro Rio de Janeiro Brazil

CONTROLAB is an environment which integrates intelligent systems and control algorithms aiming at applications in the area of robotics. Within CONTROLAB, two neural network architectures based on the backpropagation and the recursive models are proposed for the implementation of a robust speaker-independent word recognition system. The robustness of the system using the backpropagation network has been largely verified through use by children and adults in a totally uncontrolled environment such as large public halls for the exhibition of new technology products. Initial experimental results with the recursive network show that it will be able to overcome the backpropagation network major drawback, which is the frequent generation of false alarms. In addition, within CONTROLAB, a multivariable self-tuning algorithm is proposed to perform high-precision control of the trajectory, to be followed by a robot arm. High-precision control is achieved due to the ability of the algorithm to properly model the interaction forces among the robot arm joints and to cope with the non-linearities of the arm dynamics. CONTROLAB also includes computer vision facilities for object recognition and is currently running an application in which a SCARA robot arm is commanded by voice to pick up an object placed within its view area.

关键词： Control systems Intelligent control Intelligent robots backpropagation algorithms Robot control Intelligent systems Neural networks Robust control Robustness Force control

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Total least squares approach for fast learning in multilayer neural networks

Total least squares approach for fast learning in multilayer...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： R. Parisi E.D.D. Claudio G. Orlandi INFOCOM Department University of Rome E2809CLa SapienzaE2809D Rome Italy

Classical methods for training feedforward neural networks are characterized by a number of shortcomings, first of all the slow rate of convergence and the occurrence of local minima. In this paper a new learning algorithm is presented as a faster alternative to the backpropagation method. The algorithm is based on the solution of a linearized system for each layer of the network performed by a block total least squares technique. Simulation results are reported showing the high convergence speed of the new algorithm and its high degree of accuracy.

关键词： Multi-layer neural network Least squares methods Intelligent networks Neural networks backpropagation algorithms Feedforward neural networks Convergence Artificial neural networks Linear systems Matrix decomposition

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A comparison of criterion functions for a neural network applied to binary detection

A comparison of criterion functions for a neural network app...

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International Conference on Neural Networks

作者： D. Andina J.L. Sanz-Gonzalez J.A. Jimenez-Pajares Departamento de Senales Sistemas y Radiocomunicaciones ETSI de Telecomunicación Universidad Politécnica de Madrid Madrid Spain

In this paper, the performance of a neural binary detector for five different criterion functions is analyzed, showing that a typical backpropagation algorithm that uses least-mean-squares (LMS) criterion function, despite of its widely use, is far from achieving the best solution for this problem. By evaluating the detection performance of each network, for the same structure, it is shown how the change of the criterion function improves significantly the solution achieved by the typical LMS error criterion.

关键词： Neural networks Least squares approximation Detectors Performance analysis Algorithm design and analysis backpropagation algorithms

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Training recurrent networks using Hessian information

Training recurrent networks using Hessian information

引用

IEEE International Symposium on Information Theory

作者： P.H.G. Coelho Campus do Bacanga DEEE-UFMA Sao Luis Maranhao Brazil

New training algorithms for fully recurrent neural networks are presented. They are based on Hessian matrices estimates. Simulation results show that the algorithms yields satisfactory results.

关键词： backpropagation algorithms Recurrent neural networks Computer networks Cost function Computer architecture Neural networks Neurons Computational complexity Approximation algorithms Electronic mail

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Robust learning algorithms for multi-layer perceptrons with discretized synaptic weights

Robust learning algorithms for multi-layer perceptrons with ...

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International Conference on Neural Networks

作者： A. Neubauer Department of Communication Engineering Duisburg Gerhard-Mercator-University Duisburg Germany

The multi layer perceptron is one of the most popular artificial neural networks with applications to e.g. signal and image processing as well as pattern recognition and classification. In order to implement this network and the corresponding learning algorithm in electronic or optoelectronic hardware, the synaptic weights have to be discretized. This discretization, however, poses difficulties for the successful training of a multi layer perceptron. Variants of the FLETCHER-REEVES algorithm are compared to the genetic algorithm as learning strategies for the multi layer perceptron with discretized synaptic weights. Simulation results with the XOR problem as benchmark are given.

关键词： Robustness Multilayer perceptrons backpropagation algorithms Artificial neural networks Neural network hardware Signal processing Image processing Pattern recognition Genetic algorithms

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A hierarchical fractal net for pattern classification

A hierarchical fractal net for pattern classification

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International Conference on Neural Networks

作者： B. Chakraborty Y. Sawada Research Center for Electrical Communication University of Tohoku Sendai Japan

Hierarchical nets having sparse and localized connectivity with fractal connections within layers have been studied. The performance of the proposed net in classification problems has been compared to that of a fully connected multilayer perceptron and a randomly connected sparse net with an artificially generated fractal data set and a real data set derived from sonar signals for underwater target recognition. A simple version of the backpropagation algorithm has been used to train all the nets. The fractal net seems to be far better than the randomly connected sparse net in fractal pattern recognition. For the second data set the fractally connected net performs well compared to the fully connected net as fractal dimension is increased above 0.75. Moreover the fractal net seems to possess more generalization capability compared to the fully connected net in recognizing patterns other than training patterns.

关键词： Fractals Pattern classification Pattern recognition Multilayer perceptrons Signal generators Sonar Target recognition backpropagation algorithms

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Investigation of generalization ability by using noise to enhance MLP performance

Investigation of generalization ability by using noise to en...

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International Conference on Neural Networks

作者： Y. Tsukuda H. Kurokawa S. Mori Department of Elect rical Engineering Keio University Yokohama Japan

The multilayer perceptron (MLP) is successfully used in many nonlinear signal processing applications. The backpropagation learning algorithm is very useful for various problems. But the MLP obtains low generalization ability if the number of hidden units is very large in training. In this paper, the authors show that if the MLP is trained with adding noise to hidden units, it obtains good generalization ability for any number of hidden units.

关键词： Signal processing algorithms backpropagation algorithms Noise generators Nonhomogeneous media Signal processing Pattern recognition Ear

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