Design of momentum LMS adaptive strategy for parameter estimation of Hammerstein controlled autoregressive systems

• Published in: Neural computing & applications Vol. 30; no. 4; pp. 1133 - 1143
• Main Authors: Chaudhary, Naveed Ishtiaq, Zubair, Syed, Raja, Muhammad Asif Zahoor
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2018; Springer Nature B.V
• Subjects: Adaptive systems; Algorithms; Artificial Intelligence; Autoregressive models; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computing time; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Nonlinear control; Nonlinear systems; Original Article; Parameter estimation; Probability and Statistics in Computer Science; System identification; Signal processing; Momentum LMS; System identification; Hammerstein models; Adaptive algorithms
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-016-2762-1

In the present work, strength of momentum least mean square (MLMS) algorithm is exploited for nonlinear system identification problems represented with Hammerstein model. The MLMS algorithm uses the previous gradient information to estimate the current weights instead of using only current value of gradient; thus, it is faster in the convergence and less probable to trap in local minima. The perfection of the design scheme is certified through effective parameter estimation of nonlinear Hammerstein control autoregressive models. The robustness of the scheme is established by examining the performance for different levels of noise variance. The performance comparison through mean square error and Nash–Sutcliffe efficiency parameters, calculated for sufficiently large number of multiple runs, proves the intrinsic worth of MLMS algorithm in system identification.