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This paper presents a sliding-window approximation-based fractional least mean square (FLMS) algorithm for parameter estimation of Hammerstein nonlinear autoregressive moving average system with exogenous noise. The FLMS algorithm available in the literature makes use of data available at the current iteration only (or memory-less algorithm). This results in poor convergence rate of the algorithm,...
In this study, the strength of fractional signal processing is exploited in designing fractional adaptive algorithms for parameter estimation of input nonlinear Box–Jenkins (INBJ) systems. The idea is to develop fractional least mean square (F-LMS) and auxiliary model F-LMS (AM-FLMS) algorithms with three values of fractional order to adopt the variables of INBJ system for different scenarios based...
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