Improved feature least mean square algorithm

Autor(es)

Yazdanpanah, Hamed

• Tipo de documento: Artigo Científico
• Fonte: INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING; v. N/A, p. 11-pg., 2022-11-17.

Resumo

In this paper, we propose the improved feature least-mean-square (IF-LMS) algorithm to exploit hidden sparsity in unknown systems. Recently, the feature least-mean-square (F-LMS) algorithm has been introduced, but its application is limited to particular systems since it uses predetermined feature matrices. However, the proposed IF-LMS algorithm utilizes the stochastic gradient descent (SGD) method to learn feature matrices; thus, it can be used in any system that the classical LMS algorithm is applicable. Hence, by employing a learnable feature matrix, the IF-LMS algorithm has a vast application area as compared to the F-LMS algorithm. Moreover, mathematically, we discuss some parameters of the IF-LMS algorithm. Simulation results, in synthetic and real-life scenarios, demonstrate that the IF-LMS algorithm has superior filtering accuracy to the well-known LMS algorithm. (AU)

• Processo FAPESP: 19/06280-1 - Integração, transformação, aumento de dados e controle de qualidade para representações intermediárias
• Beneficiário: Hamed Yazdanpanah
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 15/22308-2 - Representações intermediárias em Ciência Computacional para descoberta de conhecimento
• Beneficiário: Roberto Marcondes Cesar Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático