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Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction.

IEEE transactions on neural networks·1996
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Evolving space-filling curves to distribute radial basis functions over an input space.

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Related Experiment Video

Updated: Jul 7, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

Genetic evolution of radial basis function coverage using orthogonal niches.

B A Whitehead1

  • 1Tennessee Univ. Space Inst., Tullahoma, TN.

IEEE Transactions on Neural Networks
|January 1, 1996
PubMed
Summary

Genetic competition creates effective radial basis function (RBF) networks. This niche-based credit sharing method improves RBF network performance for time series prediction compared to other techniques.

Related Experiment Videos

Last Updated: Jul 7, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Radial basis function (RBF) networks are powerful tools for modeling complex systems.
  • Optimizing RBF network performance often involves selecting and configuring individual RBFs.
  • Existing methods for RBF network optimization have limitations in efficiency and predictive accuracy.

Purpose of the Study:

  • To introduce a novel genetic algorithm for optimizing RBF networks.
  • To enhance RBF network prediction performance using a niche-based credit apportionment strategy.
  • To compare the proposed method against established techniques like orthogonal least squares and k-means clustering.

Main Methods:

  • Developing a genetic algorithm that simulates competition among individual RBFs.
  • Utilizing singular value decomposition (SVD) to derive orthogonal niches.
  • Implementing a credit sharing mechanism to localize competition within these niches.
  • Apportioning credit for network performance based on niche coverage.

Main Results:

  • The genetic algorithm successfully generated well-performing RBF networks.
  • Niche-based credit apportionment facilitated effective competition and data coverage.
  • The proposed RBF networks demonstrated superior prediction performance on the Mackey-Glass chaotic time series.
  • The method outperformed both orthogonal least squares and k-means clustering in prediction accuracy.

Conclusions:

  • Genetic competition within orthogonal niches is an effective strategy for RBF network optimization.
  • Niche-based credit apportionment enhances the ability of RBF networks to model complex data.
  • This approach offers a promising alternative for improving time series prediction accuracy in RBF networks.