Article

Using a mahalanobis-like distance to train radial basis neural networks

Authors:

IWANN'05: Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems

Pages 257 - 263

https://doi.org/10.1007/11494669_32

Published: 08 June 2005 Publication History

Abstract

Radial Basis Neural Networks (RBNN) can approximate any regular function and have a faster training phase than other similar neural networks. However, the activation of each neuron depends on the euclidean distance between a pattern and the neuron center. Therefore, the activation function is symmetrical and all attributes are considered equally relevant. This could be solved by altering the metric used in the activation function (i.e. using non-symmetrical metrics). The Mahalanobis distance is such a metric, that takes into account the variability of the attributes and their correlations. However, this distance is computed directly from the variance-covariance matrix and does not consider the accuracy of the learning algorithm. In this paper, we propose to use a generalized euclidean metric, following the Mahalanobis structure, but evolved by a Genetic Algorithm (GA). This GA searches for the distance matrix that minimizes the error produced by a fixed RBNN. Our approach has been tested on two domains and positive results have been observed in both cases.

References

[1]

J. E. Moody and C. Darken. Fast learning in networks of locally tuned processing units. Neural Computation, 1:281-294, 1989.

Crossref

Google Scholar

[2]

Joydeep Ghosh and Arindam Nag. An Overview of Radial Basis Function Networks. R. J. Howlett and L.C. Jain (Eds). Physica Verlag, 2000.

Google Scholar

[3]

D. S. Broomhead and D. Lowe. Multivariable functional interpolation and adaptative networks. Complex Systems, 2:321-355, 1988.

Google Scholar

[4]

M. Powell. The theory of radial basis function approximation in 1990. Advances in Numerical Analysis, 3:105-210, 1992.

Google Scholar

[5]

J. Park and I. W. Sandberg. Universal approximation and radial-basis-function networks. Neural Computation, 5:305-316, 1993.

Crossref

Google Scholar

[6]

C. G. Atkenson, A. W. Moore, and S. Schaal. Locally weighted learning. Artificial Intelligence Review, 11:11-73, 1997.

Crossref

Google Scholar

[7]

J. T. Tou and R. C. Gonzalez. Pattern Recognition Principles. Addison-Wesley, 1974.

Google Scholar

[8]

S. Weisberg. Aplied Linear Regression. New York: John Wiley and Sons, 1985.

Google Scholar

[9]

F. Babiloni, L. Bianchi, F. Semeraro, J. del R-Millan, J. Mourino, A. Cattini, S. Salinari, M. G. Marciani, and F. Cincotti. Mahalanobis distance-based classifiers are able to recognize eeg patterns by using few eeg electrodes. In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE, volume 1, pages 651-654, 2001.

Google Scholar

[10]

John H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, 1975.

Google Scholar

[11]

A. Leonardis and H. Bischof. An efficient mdl-based construction of rbf networks. Neural Networks, 11:963-973, 1998.

Crossref

Google Scholar

[12]

M. J. L. Orr. Introduction to radial basis neural networks. Technical Report. Centre for Cognitive Science, University of Edinburgh, 1996.

Google Scholar

[13]

L. Yingwei, N. Sundararajan, and P. Saratchandran. A sequential learning scheme for function approximation using minimal radial basis function neural networks. Neural Computation, 9:461-478, 1997.

Crossref

Google Scholar

Cited By

View all

Singh DKumar V(2018)Comprehensive survey on haze removal techniquesMultimedia Tools and Applications10.1007/s11042-017-5321-677:8(9595-9620)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s11042-017-5321-6
Praga-Alejo RTorres-Treviño LGonzález-González DAcevedo-Dávila JCepeda-Rodríguez F(2012)Analysis and evaluation in a welding process applying a Redesigned Radial Basis FunctionExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.02.15439:10(9669-9675)Online publication date: 1-Aug-2012
https://dl.acm.org/doi/10.1016/j.eswa.2012.02.154
Praga-Alejo RTorres-Treviño LGonzález DAcevedo-Dávila JCepeda F(2010)A radial basis function redesigned for predicting a welding processProceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II10.5555/1927099.1927125(257-268)Online publication date: 8-Nov-2010
https://dl.acm.org/doi/10.5555/1927099.1927125
Show More Cited By

Using a mahalanobis-like distance to train radial basis neural networks
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
  2. Machine learning
    1. Machine learning approaches

Recommendations

Identification and control using MLP, Elman, NARXSP and radial basis function networks: a comparative analysis

This paper describes four neural networks multilayer perceptron (MLP) network, Elman network, NARXSP network and radial basis function (RBF) network. Neural networks are applied for identification and control of DC servo motor and benchmark nonlinear ...
Stock Indices Prediction Using Radial Basis Function Neural Network
Swarm, Evolutionary, and Memetic Computing
Abstract
Aim of the paper is to efficiently predict the stock market data for future days ahead using Radial Basis Function (RBF) neural network. DJIA and S&P 500 stock indices have been taken to simulate the RBF model and also comparison has been done ...
Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in Data Classification

This paper introduces a learning algorithm that can be used for training reformulated radial basis function neural networks (RBFNNs) capable of identifying uncertainty in data classification. This learning algorithm trains a special class of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

IWANN'05: Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems

June 2005

1253 pages

ISBN:3540262083

Editors:
Joan Cabestany
Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, Barcelona, Spain
,
Alberto Prieto
Department of Computer Architecture and Computer Technology, University of Granada, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, Barcelona
,
Francisco Sandoval
Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, Málaga, Spain

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 08 June 2005

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Singh DKumar V(2018)Comprehensive survey on haze removal techniquesMultimedia Tools and Applications10.1007/s11042-017-5321-677:8(9595-9620)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s11042-017-5321-6
Praga-Alejo RTorres-Treviño LGonzález-González DAcevedo-Dávila JCepeda-Rodríguez F(2012)Analysis and evaluation in a welding process applying a Redesigned Radial Basis FunctionExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.02.15439:10(9669-9675)Online publication date: 1-Aug-2012
https://dl.acm.org/doi/10.1016/j.eswa.2012.02.154
Praga-Alejo RTorres-Treviño LGonzález DAcevedo-Dávila JCepeda F(2010)A radial basis function redesigned for predicting a welding processProceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II10.5555/1927099.1927125(257-268)Online publication date: 8-Nov-2010
https://dl.acm.org/doi/10.5555/1927099.1927125
Tikka J(2007)Input selection for radial basis function networks by constrained optimizationProceedings of the 17th international conference on Artificial neural networks10.5555/1776814.1776841(239-248)Online publication date: 9-Sep-2007
https://dl.acm.org/doi/10.5555/1776814.1776841

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Cited By

Recommendations

Identification and control using MLP, Elman, NARXSP and radial basis function networks: a comparative analysis

Stock Indices Prediction Using Radial Basis Function Neural Network

Training Reformulated Radial Basis Function Neural Networks Capable of Identifying Uncertainty in Data Classification

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations