Fuzzy-neural models for real-time identification and control of a mechanical system

被引：0
|
作者：
Baruch, IS
Flores, JM
Martínez, JC
Nenkova, B
机构：
[1] IPN, CINVESTAV, Mexico City 07360, DF, Mexico
[2] BAS, IIT, Sofia, Bulgaria
来源：
ARTIFICIAL INTELLIGENCE: METHODOLOGY, SYSTEMS, APPLICATIONS, PROCEEDINGS | 2000年 / 1904卷
关键词：
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：
A two-layer Recurrent Neural Network Model (RNNM) and an improved Backpropagation-through-time method of its learning are described. For a complex nonlinear plants identification, a fuzzy-neural multi-model, is proposed. The proposed fuzzy-neural model, containing two RNNMs is applied for real-time identification of nonlinear mechanical system. The simulation and experimental results confirm the RNNM applicability.
引用
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页码：292 / 300
页数：9
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