Modeling hysteresis and its inverse model using neural networks based on expanded input space method

被引：65

作者：

Zhao, Xinlong ^{[1
]}

Tan, Yonghong ^{[2
]}

机构：

[1] Zhejiang Sci Tech Univ, Inst Automat, Hangzhou 310018, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Elect Engn, Chengdu 610054, Peoples R China

来源：

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY | 2008年 / 16卷 / 03期

基金：

中国国家自然科学基金;

关键词：

hysteresis; hysteretic operator; inverse model; modeling; neural networks;

D O I：

10.1109/TCST.2007.906274

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 neural network-based approach of identification for hysteresis and its inverse model is proposed. In this method, a hysteretic operator is proposed to extract the change tendency of hysteresis. Then, an expanded input space is constructed to transform the multivalued mapping into one-to-one mapping so that the neural networks are capable of implementing identification for hysteresis. Similar to the method of modeling hystereis, an inverse hyteretic operator is proposed to construct an inverse model for hysteresis. Then the experimental results are presented to illustrate the potential of the proposed modeling technique.

引用

页码：484 / 490

页数：7

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