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Variable selection in non-linear systems modelling
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DOI：10.1006/mssp.1998.0180.png)
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摘要
En 中文
A new algorithm which preselects variables in non-linear system models is introduced by converting the problem into a variable selection procedure for a set of linearised models. Because on this result an algorithm which consists of a cluster analysis linearisation sub-region division procedure, a linear subset selection routine using an all possible regression algorithm and a genetic algorithm is developed. This algorithm can be applied to the modelling of non-linear systems using a wide class of model forms including the non-linear polynomial model, the non-linear rational model, artificial neural networks and others. Numerical simulations are included to demonstrate the efficiency of the new algorithm. (C) 1999 Academic Press.
Keyword:
LEAST-SQUARES ALGORITHM
NEURAL NETWORKS
MUTUAL INFORMATION
FAULT-DIAGNOSIS
IDENTIFICATION
CLASSIFICATION
