Titlebook: Hybrid Self-Organizing Modeling Systems; Godfrey C. Onwubolu Book 2009 Springer-Verlag Berlin Heidelberg 2009 algorithm.algorithms.artific

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ALOFT

Book 2009 systems. However, it is known to often under-perform on non-parametric regression tasks, while time series modeling GMDH exhibits a tendency to find very complex polynomials that cannot model well future, unseen oscillations of the series. In order to alleviate these problems, GMDH has been recentl

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MODE

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VERT

Hybrid Computational Intelligence and GMDH Systems,ry complex polynomials that cannot model well future, unseen oscillations of the series. In order to alleviate the problems associated with standard GMDH approach, a number of researchers have attempted to hybridize GMDH with some evolutionary optimization techniques. This is the central theme of th

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肉體

Hybrid Genetic Algorithm and GMDH System, design the coefficients as well as the connectivity configuration of GMDH-type neural networks used for modelling and prediction of various complex models in both single and multi-objective Pareto based optimization processes. Such generalization of network’s topology provides near optimal networks