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A new discrete-continuous algorithm for radial basis function networks construction.

Long Zhang, Kang Li, Haibo He

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    |May 9, 2014
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    Summary
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    This study introduces a novel discrete-continuous algorithm for constructing radial basis function (RBF) networks, improving efficiency and performance. The method optimizes model parameters simultaneously, outperforming existing techniques in computational speed and classification accuracy.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computational Science

    Background:

    • Radial basis function (RBF) network construction requires determining model size, hidden nodes, and output weights.
    • Existing methods like least squares and gradient methods have limitations in optimizing RBF network parameters, often leading to slow convergence or suboptimal results.
    • Current algorithms may neglect the correlations between hidden nodes and output weights, hindering efficient parameter optimization.

    Purpose of the Study:

    • To propose a new discrete-continuous algorithm for constructing RBF models.
    • To enhance the efficiency and accuracy of RBF network training by addressing parameter optimization challenges.
    • To develop a computationally efficient method that considers the interdependencies between RBF network parameters.

    Main Methods:

    • A novel algorithm combining orthogonal least squares (OLS)-based forward stepwise selection for initial model construction and Levenberg-Marquardt (LM)-based optimization for parameter refinement.
    • The method incorporates a unique approach to translate output weights as dependent parameters using OLS to leverage correlations between hidden nodes and output weights.
    • An equivalent recursive sum of squared error is derived to reduce computational demands for LM method's first derivatives.

    Main Results:

    • The proposed algorithm demonstrates significantly improved computational efficiency compared to the Continuous Forward Algorithm (CFA).
    • Numerical examples confirm the effectiveness of the new method in constructing RBF models.
    • Friedman statistical tests on 13 classification problems show that RBF networks built with this method are highly competitive against popular classifiers.

    Conclusions:

    • The developed discrete-continuous algorithm offers a more efficient and effective approach to RBF network construction.
    • Simultaneous optimization of all parameters, considering their correlations, leads to superior performance.
    • The method presents a promising alternative for building competitive RBF network models for classification tasks.