Design of an auto-associative neural network by using design of experiments approach

被引：0

作者：

Bratina, Bozidar ^{[1
]}

Muskinja, Nenad ^{[1
]}

Tovornik, Boris ^{[1
]}

机构：

[1] Univ Maribor, Fac Elect Engn & Comp Sci, SLO-2000 Maribor, Slovenia

来源：

KNOWLEDGE - BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS | 2008年 / 5177卷

关键词：

fault detection and isolation; nonlinear principle components; neural networks; design of experiments;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Data driven Computational intelligence methods have become popular in Fault detection and isolation (FDI) due to relatively quick design and not so difficult implementation on real systems. In this paper a research work on a Taguchi DoE approach for training the auto-associative neural network to extract non-linear principal components of a system, is presented. Design of such network was first proposed by Kramer however for achieving robustness to unspecified parameters such as noise level and disturbances, a design of experiments methodology can be used to optimally define network structure and parameters.

引用

页码：25 / 32

页数：8

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