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
收藏
页码：5534 / 5554
页数：21
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