Input-output modelling with decomposed neuro-fuzzy ARX model

被引：9

作者：

Golob, Marjan ^{[1
]}

Tovornik, Boris ^{[1
]}

机构：

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

来源：

NEUROCOMPUTING | 2008年 / 71卷 / 4-6期

关键词：

input-output modelling; fuzzy ARX model; neuro-fuzzy system;

D O I：

10.1016/j.neucom.2007.02.011

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a new neuro-fuzzy system based model, which is useful for the modelling of nonlinear dynamic systems. The new proposed model constitutes a soft computing method, namely, reasoning with a fuzzy inference system (FIS) and tin optimisation by the neural-network learning algorithm. A structure, named the decomposed neuro-fuzzy ARX model is proposed. This structure is based on decomposition of the FIS. An evolution of a learning algorithm for the decomposed fuzzy model is suggested. A comparative study of dynamic system identification using conventional FIS models and the proposed neuro-fuzzy ARX model is presented for Box-Jenkins data set. (c) 2007 Elsevier B.V. All rights reserved.

引用

页码：875 / 884

页数：10

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