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Efficient training of RBF neural networks for pattern recognition.

F Lampariello1, M Sciandrone

  • 1Istituto di Analisi dei Sistemi ed Informatica, CNR, 00185 Rome, Italy. lampariello@iasi.rm.cnr.it

IEEE Transactions on Neural Networks
|February 6, 2008
PubMed
Summary

This study introduces a novel training method for Radial Basis Function (RBF) neural networks in pattern recognition. The new approach improves computational efficiency for distinguishing data sets.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Training Radial Basis Function (RBF) neural networks is crucial for pattern recognition tasks.
  • Classical methods often use least-squares error functions, which may not be optimal for classification.
  • Classification requires network outputs to exceed or fall below a specific threshold.

Purpose of the Study:

  • To propose an alternative training approach for RBF neural networks in classification.
  • To develop a method that addresses the specific needs of classification problems.
  • To improve the efficiency of RBF network training.

Main Methods:

  • Formulating the RBF training problem as a system of nonlinear inequalities.
  • Defining a novel error function that focuses on violated inequalities.

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  • Developing a training algorithm based on this inequality formulation.
  • Comparing the proposed algorithm with the traditional least-squares method.

    • Main Results:

    • The proposed approach demonstrates effectiveness in RBF network training for pattern recognition.
    • The new method shows significant savings in computational time compared to least-squares.
    • The algorithm successfully trains RBF networks for distinguishing between two disjoint sets.

    Conclusions:

    • The proposed inequality-based training method is effective for RBF neural networks in classification.
    • This approach offers a computationally efficient alternative to least-squares methods.
    • The findings highlight the potential for improved RBF network training algorithms.