FEATURE NORMALIZED LMS ALGORITHMS

Autor(es)

Yazdanpanah, Hamed ; Apolinario, Jose A., Jr. ; Matthews, MB

• Tipo de documento: Artigo Científico
• Fonte: CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS; v. N/A, p. 4-pg., 2019-01-01.

Resumo

Recently, a great effort has been observed to exploit sparsity in physical parameters; however, in many cases, sparsity is hidden in relations between parameters, and some appropriate tools should be utilized to expose it. In this paper, a family of algorithms called feature normalized least-mean-square (F-NLMS) algorithms is proposed to exploit hidden sparsity. The major key of these algorithms is a so-called feature matrix that transforms non-sparse systems to new systems containing sparsity; then the revealed sparsity is exploited by some sparsity-promoting penalty function. Numerical results demonstrate that the F-NLMS algorithms can reduce the steady-state mean-squared-error (MSE) and/or improve the convergence rate of the learning process significantly. (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