Study of Sparsity-Aware Set-Membership Adaptive Algorithms with Adjustable Penalties

2017/08/05 by André R. Flores, Flores, André, Rodrigo C. de Lamare +1
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.1708.01696

openalex publication_date 2017/08/05 · openalex created_date 2017/08/17 · openalex updated_date 2026/07/28

Abstract

In this paper, we propose sparsity-aware data-selective adaptive filtering algorithms with adjustable penalties. Prior work incorporates a penalty function into the cost function used in the optimization that originates the algorithms to improve their performance by exploiting sparsity. However, the strength of the penalty function is controlled by a scalar that is often a fixed parameter. In contrast to prior work, we develop a framework to derive algorithms that automatically adjust the penalty function parameter and the step size to achieve a better performance. Simulations for a system identification application show that the proposed algorithms outperform in convergence speed existing sparsity-aware algorithms.

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