MODEL VALIDATION TESTS FOR MULTIVARIABLE NONLINEAR MODELS INCLUDING NEURAL NETWORKS

被引：60

作者：

BILLINGS, SA ^{[1
]}

ZHU, QM ^{[1
]}

机构：

[1] UNIV BRIGHTON,DEPT MECH & MFG ENGN,BRIGHTON BN2 4GJ,E SUSSEX,ENGLAND

来源：

INTERNATIONAL JOURNAL OF CONTROL | 1995年 / 62卷 / 04期

关键词：

D O I：

10.1080/00207179508921566

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A fast and concise MIMO nonlinear model validity test procedure is derived, based on higher order correlation functions, to form a global-to-local hierarchical validation diagnosis of identified MIMO linear and nonlinear models. The new procedure is applied to four MIMO nonlinear system models including a neural network training example, to demonstrate the effectiveness of the tests.

引用

页码：749 / 766

页数：18

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