Vol. 6 No. 1 (2019)

Articles

System Identification and Model Validation of Recursive Least Squares Algorithm for Box–Jenkins Systems

Published:

December 23, 2019

https://doi.org/10.17667/riim.2019.1/4.

Authors

Nasar Aldian Ambark Shashoa,

Electrical and Electronics Engineering Department, Azzaytuna University

Abdurrezag S. Elmezughi,

Computer Engineering Department, Azzaytuna University

Ibrahim N. Jleta,

Department of Electrical Engineering, Libyan Academy of Graduate Studies

Nasser B. Ekreem

Computer Engineering Department, Azzaytuna University

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Keywords

system identification model validation parameter estimation histogram Box–Jenkins system

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Copyright (c) 2019 by the authors

This work is licensed under a Creative Commons Attribution 4.0 International License.

How To Cite

Selected Style: APA

Aldian Ambark Shashoa, N., S. Elmezughi, A., N. Jleta, I., & B. Ekreem, N. (2019). System Identification and Model Validation of Recursive Least Squares Algorithm for Box–Jenkins Systems. Recent Innovations in Mechatronics, 6(1), 1-6. https://doi.org/10.17667/riim.2019.1/4.

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Abstract

In this paper, a new-type recursive least squares algorithm is proposed for identifying the system model parameters and the noise model parameters of Box–Jenkins Systems. The basic idea is based on replacing the unmeasurable variables in the information vectors with their estimates. The proposed algorithm has high computational efficiency because the dimensions of the involved covariance matrices in each subsystem become small. Validation of the model is evaluated using some statistical methods, Which, best-fit criterion and Histogram. Simulation results are presented to illustrate the effectiveness of the proposed algorithm.

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