Neuro-fuzzy identification models

被引：0

作者：

Matko, D ^{[1
]}

Karba, R ^{[1
]}

Zupancic, B ^{[1
]}

机构：

[1] Univ Ljubljana, Fac Elect Engn, Ljubljana, Slovenia

来源：

PROCEEDINGS OF IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY 2000, VOLS 1 AND 2 | 2000年

关键词：

convergence; fuzzy models; neural network models; nonlinear models; noise characteristics;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The paper deals with the Neural Net and Fuzzy Models as universal approximators. Four types of models suitable for identification are presented: The Nonlinear Output Error, The Nonlinear Input Error, The Nonlinear Generalised Output Error and The Nonlinear Generalised Input Error Model. The convergence properties of all four models in the presence of disturbing noise are reviewed and it is shown that the condition for an unbiased identification is that the disturbing noise is white and that it enters the nonlinear model in specific point depending on the type of the model.

引用

页码：650 / 655

页数：6

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