Evolving RBF neural networks for time-series forecasting with EvRBF
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
EvRBF: evolving RBF neural networks for classification problems
AIC'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Informatics and Communications - Volume 7
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In this paper a study of two approaches of a meta-algorithm, Meta_CHC_RBF, is presented. The main goal of this algorithm is to automatically design Radial Basis Function Networks (RBFNs) finding a suitable configuration of parameters (automatically adapted to every problem) necessary for the algorithm EvRBF, an evolutionary algorithm for the automatic design of asymmetric RBFNs. The principal difference between two proposals is the type of codification, in the fist one, the meta-algorithm uses binary codification, while in the second one, it implements real codification; affecting this influence of the codification kind in the carried out experimentation. Finally, results show that the first approach yields good marks reducing the computation time, with respect the second one.