Evolutionary Combining of Basis Function Neural Networks for Classification

Authors:
César Hervás;Francisco Martínez;Mariano Carbonero;Cristóbal Romero;Juan Carlos Fernández
Affiliations:
Department of Computing and Numerical Analysis University of Córdoba, Spain,;Department of Management and Quantitative Methods, ETEA, Spain,;Department of Management and Quantitative Methods, ETEA, Spain,;Department of Computing and Numerical Analysis University of Córdoba, Spain,;Department of Computing and Numerical Analysis University of Córdoba, Spain,
Venue:
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Year:
2007

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Abstract

The paper describes a methodology for constructing a possible combination of different basis functions (sigmoidal and product) for the hidden layer of a feed forward neural network, where the architecture, weights and node typology are learned based on evolutionary programming. This methodology is tested using simulated Gaussian data set classification problems with different linear correlations between input variables and different variances. It was found that combined basis functions are the more accurate for classification than pure sigmoidal or product-unit models. Combined basis functions present competitive results which are obtained using linear discriminant analysis, the best classification methodology for Gaussian data sets.