| Texto completo | |
| Autor(es): |
Yazdanpanah, Hamed
Número total de Autores: 1
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| 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 |