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Variable selection in non-linear systems modelling

delete1999-03-01
delete18
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K
Kezhi Mao *
DOI：10.1006/mssp.1998.0180delete
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Abstract

Abstract

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.
Keywords:
LEAST-SQUARES ALGORITHM
NEURAL NETWORKS
MUTUAL INFORMATION
FAULT-DIAGNOSIS
IDENTIFICATION
CLASSIFICATION

Journal

Mechanical Systems and Signal Processing cover
Mechanical Systems and Signal Processing
IF:
8.9
Papers:
1.3W
Citations:
6.6W

Organization

No organization information available
Cited Papers

Cited Papers

No cited papers available