Error-Compensated Marginal Linearization Method for Modeling a Fuzzy System

被引：7
作者：
Wang, De-Gang [1 ,2 ]
Chen, C. L. Philip [3 ]
Song, Wen-Yan [4 ]
Li, Hong-Xing [1 ]
机构：
[1] Dalian Univ Technol, Sch Control Sci & Engn, Dalian 116024, Peoples R China
[2] Informedia Elect Co Ltd, Dalian 116001, Peoples R China
[3] Univ Macau, Fac Sci & Technol, Macau 99999, Peoples R China
[4] Dongbei Univ Finance & Econ, Dept Quantitat Econ, Dalian 116025, Peoples R China
来源：
IEEE TRANSACTIONS ON FUZZY SYSTEMS | 2015年 / 23卷 / 01期
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Error-compensated marginal linearization (EMALINE) method; fuzzy system; nonlinear system; self-organization fuzzy systems; universal approximation; OUTPUT-FEEDBACK CONTROL; NEURAL-NETWORKS; NONLINEAR-SYSTEMS; UNIVERSAL APPROXIMATORS; INFERENCE; LOGIC; INFORMATION; ALGORITHM; DESIGN; SCHEME;
D O I：
10.1109/TFUZZ.2014.2306952
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a novel Error-compensated MArginal LINEarization (EMALINE) fuzzy modeling method is proposed. This method models a group of data information to a piecewise linear fuzzy system with high accuracy within a given error bound. It is proved that the fuzzy system generalized by the EMALINE method possesses universal approximation capability for a class of nonlinear systems. In addition, the theoretical approximation error bounds of the fuzzy system generalized by the EMALINE method are established and proved. Theoretical and practical results indicate that the EMALINE has better approximation accuracy than those of previous approaches. Numerical examples are shown to illustrate the validity of the proposed approach.
引用
收藏
页码：215 / 222
页数：8
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