The variable step‐size LMS/F algorithm using nonparametric method for adaptive system identification

• Published in: International journal of adaptive control and signal processing Vol. 34; no. 12; pp. 1799 - 1811
• Main Authors: Patnaik, Ansuman, Nanda, Sarita
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.12.2020; Wiley Subscription Services, Inc
• Subjects: Adaptive algorithms; Adaptive systems; Algorithms; Convergence; Gaussian noise; impulsive noise; Noise; Nonlinear systems; nonparametric; Nonparametric statistics; Random noise; System identification; variable step‐size; Volterra modeling
• ISSN: 0890-6327; 1099-1115
• DOI: 10.1002/acs.3185

Summary A fundamental challenge affecting the performance of a system is the undesired effect of noise on the system. Practically, real‐time systems are influenced by Gaussian noise and impulsive noise. Identification of these nonlinear physical systems in the presence of noise offers broader applications than linear system identification. Hence, this article introduces a variable step‐size technique to solve the conflicting requirement of rapid convergence and low mean square error (MSE) in the presence of both Gaussian and impulsive noise. Moreover, to avoid over parameterized equations existing in the variable step‐size equation, this article proposes the nonparametric variable step‐size (NPVSS), which depends on error estimates at instants of time and is used with the least mean square/fourth (LMS/F) algorithm. The computational complexity analysis, computer simulations, and implementation in real‐time setup validate that the proposed NPVSS‐LMS/F algorithm provides superior performance in terms of convergence time and MSE compared to the existing algorithms for both linear and nonlinear system identification in the presence of noise.