Design of fractional adaptive strategy for input nonlinear Box-Jenkins systems

被引：44

作者：

Chaudhary, Naveed Ishtiaq ^{[1
]}

Raja, Muhammad Asif Zahoor ^{[2
]}

机构：

[1] Int Islamic Univ, Dept Elect Engn, Islamabad, Pakistan

[2] COMSATS Inst Informat Technol, Dept Elect Engn, Attock, Pakistan

来源：

SIGNAL PROCESSING | 2015年 / 116卷

关键词：

Parameter estimation; Adaptive filtering; Input nonlinear systems; Box-Jenkins model; Fractional LMS; Kernel LMS; Volterra LMS; PARAMETER-ESTIMATION ALGORITHMS; ITERATIVE ESTIMATION ALGORITHM; LEAST-SQUARES ALGORITHM; IDENTIFICATION METHODS; AUXILIARY MODEL; LINEAR-SYSTEMS; STATE;

D O I：

10.1016/j.sigpro.2015.04.015

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this study, the strength of fractional signal processing is exploited in designing fractional adaptive algorithms for parameter estimation of input nonlinear Box-Jenkins (INBJ) systems. The idea is to develop fractional least mean square (F-LMS) and auxiliary model F-LMS (AM-FLMS) algorithms with three values of fractional order to adopt the variables of INBJ system for different scenarios based on noise and step size variations. The comparative study of the proposed results is made with true values of the system, as well as, with the results of Volterra and Kernel LMS adaptive algorithms in order to establish the correctness of the design scheme. The accuracy and convergence of the proposed scheme is analyzed on large data set generated through statistical analysis based on sufficient number of independent runs of the algorithm and result in term of performance measures established the worth of the scheme. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：141 / 151

页数：11

共 56 条

[31] Least squares based iterative algorithms for identifying Box-Jenkins models with finite measurement data [J].