Algoritmos adaptativos LMS normalizados proporcionais: proposta de novos algoritmos para identificação de plantas esparsas

• Ano de defesa: 2016
• Autor(a) principal: CASTELO BRANCO, César Augusto Santana
• Orientador(a): SOUZA, Francisco das Chagas de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Filtragem adaptativa; Algoritmos adaptativos LMS normalizados proporcionais; Métodos de otimização; Razão áurea; Busca tabu; Identificação de plantas esparsas
• Palavras-chave em Inglês: Adaptive filtering; Proportionate normalized leas-mean-square adaptive algorithms; Optimization methods; Golden section; Tabu search; Sparse plant identification
• Área do conhecimento CNPq: Sistemas de Computação
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/1688

This work proposes new methodologies to optimize the choice of the parameters of the proportionate normalized least-mean-square (PNLMS) adaptive algorithms. The proposed approaches use procedures based on two optimization methods, namely, the golden section and tabu search methods. Such procedures are applied to determine the optimal parameters in each iteration of the adaptation process of the PNLMS and improved PNLMS (IPNLMS) algorithms. The objective function for the proposed procedures is based on the a posteriori estimation error. Performance studies carried out to evaluate the impact of the PNLMS and IPNLMS parameters in the behavior of these algorithms shows that, with the aid of optimization techniques to choose properly such parameters, the performance of these algorithms may be improved in terms of convergence speed for the identification of plants with high sparseness degree. The main goal of the proposed methodologies is to improve the distribution of the adaptation energy between the coefficients of the PNLMS and IPNLMS algorithms, using parameter values that lead to the minimal estimation error of each iteration of the adaptation process. Numerical tests performed (considering various scenarios in which the plant impulse response is sparse) show that the proposed methodologies achieve convergence speeds faster than the PNLMS and IPNLMS algorithms, and other algorithms of the PNLMS class, such as the sparseness controlled IPNLMS (SC-IPNLMS) algorithm.