Adaptive Uncerntainty Identification with Neural Network

被引：0

作者：

Zhang Yumin ^{[1
]}

Teng Fei ^{[1
]}

Liang Guiqin ^{[1
]}

Wang Zhiqiang ^{[1
]}

机构：

[1] Beihang Univ, Sch Instrumentat & Optoelect Engn, Beijing 100191, Peoples R China

来源：

2015 34TH CHINESE CONTROL CONFERENCE (CCC) | 2015年

关键词：

Adaptive Learning; Observer; Neural Network; STOCHASTIC-SYSTEMS; FAULT-DETECTION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper provides an identification method for uncertainties in system via dynamic neural networks, where the uncertainties include parameter uncertainty, disturbances, faults or system load. The incertainties here are translated into the weight matrices to be identified. To idenfication purpose, a dynamic neural network observer is designed, where weight matrices are adaptive tuned. The numerical simulation shows that the given idenificatuion algorithm is more suitable for disturbances, faults or system load. For given system load, the present algorithm can model system into multimodel mode.

引用

页码：2055 / 2059

页数：5

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