Proportionate Robust Diffusion Recursive Least Exponential Hyperbolic Cosine Algorithm: Optimum Parameter Selection and Convergence Analysis

In this brief, an improved version of the proportionate robust diffusion recursive least exponential hyperbolic cosine algorithm is suggested. This improved version is obtained by optimally selecting the step-size parameter of this algorithm using a minimization of the squared norm of the error vector. Moreover, a complete theoretical analysis of the presented algorithm is performed. This theoretical analysis consists of mean-square convergence analysis, mean-square steady-state analysis, and a discussion on the forgetting factor parameter selection. Moreover, simulation experiments show that the improved algorithm is superior than the original algorithm and other state-of-the-art algorithms in the literature in terms of speed of convergence.

Automated summary

In this paper, an improved version of the proportionate robust diffusion recursive least exponential hyperbolic cosine algorithm is proposed. The step-size parameter of this algorithm is optimally selected by minimizing the squared norm of the error vector. Theoretical analysis is performed to study the mean-square convergence, mean-square steady-state, and forgetting factor parameter selection, and simulation experiments demonstrate that the improved algorithm outperforms the original algorithm and other state-of-the-art algorithms in terms of convergence speed.