Hybrid convex combinations for IIR system identification.

• Ano de defesa: 2016
• Autor(a) principal: Ferro, Humberto Fioravante
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Adaptive filters; Análise média quadrática; Combinações de filtros; Conversão de energia elétrica; Energy conservation relation; Filters combinations; Filtros elétricos adaptativos; Filtros elétricos digitais; Identificação de sistemas; Mean square analysis; Systems identification
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/3/3142/tde-04072016-103658/

The low complexity of IIR adaptive filters (AFs) is specially appealing to realtime applications but some drawbacks have been preventing their widespread use so far. For gradient based IIR AFs, adverse operational conditions cause convergence problems in system identification scenarios: underdamped and clustered poles, undermodelling or non-white input signals lead to error surfaces where the adaptation nearly stops on large plateaus or get stuck at sub-optimal local minima that can not be identified as such a priori. Furthermore, the non-stationarity in the input regressor brought by the filter recursivity and the approximations made by the update rules of the stochastic gradient algorithms constrain the learning step size to small values, causing slow convergence. In this work, we propose IIR performance enhancement strategies based on hybrid combinations of AFs that achieve higher convergence rates than ordinary IIR AFs while keeping the stability.