Online affine model identification of nonlinear processes using a new adaptive neuro-fuzzy approach

被引：12

作者：

Salahshoor, Karim ^{[1
]}

Hamzehnejad, Morteza ^{[1
]}

Zakeri, Sepide ^{[1
]}

机构：

[1] Petr Univ Technol, Dept Automat & Instrumentat, Tehran, Iran

来源：

APPLIED MATHEMATICAL MODELLING | 2012年 / 36卷 / 11期

关键词：

Online identification; Affine model; Adaptive neuro-fuzzy model; EKF; ANFIS; SYSTEM IDENTIFICATION; NETWORKS; ANFIS;

D O I：

10.1016/j.apm.2012.01.010

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper presents a new online identification algorithm to drive an adaptive affine dynamic model for nonlinear and time-varying processes. The new algorithm is devised on the basis of an adaptive neuro-fuzzy modeling approach. Two adaptive neuro-fuzzy models are sequentially identified on the basis of the most recent input-output process data to realize an online affine-type model. A series of simulation test studies has been conducted to demonstrate the efficient capabilities of the proposed algorithm to automatically identify an online affine-type model for two highly nonlinear and time-varying continuous stirred tank reactor (CSTR) benchmark problems having inherent non-affine dynamic model representations. Adequacy assessments of the identified models have been explored using different evaluation measures, including comparison with an adaptive neuro-fuzzy inference system (ANFIS) as the pioneering and the most popular adaptive neuro-fuzzy system with powerful modeling features. (C) 2012 Elsevier Inc. All rights reserved.

引用

页码：5534 / 5554

页数：21

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