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.

中文翻译：

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使用非参数方法的可变步长LMS / F算法用于自适应系统识别

影响系统性能的根本挑战是噪声对系统的不良影响。实际上，实时系统受高斯噪声和脉冲噪声的影响。在存在噪声的情况下，这些非线性物理系统的识别比线性系统识别提供了更广泛的应用。因此，本文介绍了一种可变步长技术，以解决在存在高斯噪声和脉冲噪声的情况下快速收敛和低均方误差（MSE）的冲突要求。此外，为避免变量步长方程中存在过多的参数化方程，本文提出了非参数变量步长（NPVSS），该变量依赖于瞬时误差估计，并与最小均方/四次方（LMS）一起使用/ F）算法。计算复杂度分析