Decomposition‐based maximum likelihood gradient iterative algorithm for multivariate systems with colored noise

• Published in: International journal of robust and nonlinear control Vol. 34; no. 11; pp. 7265 - 7284
• Main Author: Liu, Lijuan
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 25.07.2024
• Subjects: Decomposition; Identification methods; Iterative algorithms; iterative identification; maximum likelihood; model decomposition; Multivariate analysis; multivariate system; negative gradient search; Parameter estimation; Principles; Subsystems
• ISSN: 1049-8923; 1099-1239
• DOI: 10.1002/rnc.7344

Summary In this paper, we use the maximum likelihood principle and the negative gradient search principle to study the identification issues of the multivariate equation‐error systems whose outputs are contaminated by an moving average noise process. The model decomposition technique is used to decompose the system into several regressive identification subsystems based on the number of the outputs. In order to improve the parameter estimation accuracy, a decomposition‐based multivariate maximum likelihood gradient iterative algorithm is proposed by means of the maximum likelihood principle and the iterative identification method. The numerical simulation example indicates that the proposed method has better parameter estimation results than the compared decomposition‐based multivariate maximum likelihood gradient algorithm.