Combined Genetic Algorithm Optimization and Regularized Orthogonal Least Squares Learning for Radial Basis Function Networks

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Combined Genetic Algorithm Optimization and Regularized Orthogonal Least Squares Learning for Radial Basis Function Networks (1999)

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by S. Chen , et al.

Citations:

40 - 14 self

BibTeX

@MISC{Chen99combinedgenetic,
    author = {S. Chen and et al.},
    title = {Combined Genetic Algorithm Optimization and Regularized Orthogonal Least Squares Learning for Radial Basis Function Networks},
    year = {1999}
}

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Abstract

The paper presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and prediction is used as an example to demonstrate the effectiveness of this hierarchical learning approach.

Keyphrases

regularized orthogonal least square learning genetic algorithm optimization radial basis function network regularized orthogonal least square nonlinear time series modeling genetic algorithm rbf width rbf network upper level regularization parameter two-level learning method hierarchical learning approach radial basis function

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