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A Novel Adaptive LMS Algorithm with Genetic Search Capabilities for System Identification of Adaptive FIR and IIR Filters

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Department of Control and Systems Engineering, University of Technology, Baghdad 10001, Iraq
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Department of Electrical Engineering, College of Engineering, University of Baghdad, Baghdad 10001, Iraq
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Department of Control and automation Engineering Techniques, Electrical Engineering Technical College, Middle Technical University, Baghdad 10001, Iraq
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Author to whom correspondence should be addressed.
Information 2019, 10(5), 176; https://doi.org/10.3390/info10050176
Received: 25 March 2019 / Revised: 12 April 2019 / Accepted: 23 April 2019 / Published: 20 May 2019
(This article belongs to the Section Artificial Intelligence)
In this paper we introduce a novel adaptation algorithm for adaptive filtering of FIR and IIR digital filters within the context of system identification. The standard LMS algorithm is hybridized with GA (Genetic Algorithm) to obtain a new integrated learning algorithm, namely, LMS-GA. The main aim of the proposed learning tool is to evade local minima, a common problem in standard LMS algorithm and its variants and approaching the global minimum by calculating the optimum parameters of the weights vector when just estimated data are accessible. In the proposed LMS-GA technique, first, it works as the standard LMS algorithm and calculates the optimum filter coefficients that minimize the mean square error, once the standard LMS algorithm gets stuck in local minimum, the LMS-GA switches to GA to update the filter coefficients and explore new region in the search space by applying the cross-over and mutation operators. The proposed LMS-GA is tested under different conditions of the input signal like input signals with colored characteristics, i.e., correlated input signals and investigated on FIR adaptive filter using the power spectral density of the input signal and the Fourier-transform of the input’s correlation matrix. Demonstrations via simulations on system identification of IIR and FIR adaptive digital filters revealed the effectiveness of the proposed LMS-GA under input signals with different characteristics. View Full-Text
Keywords: multimodal error surface; genetic algorithm; LMS algorithm; system identification; adaptive filtering; IIR filter multimodal error surface; genetic algorithm; LMS algorithm; system identification; adaptive filtering; IIR filter
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Humaidi, A.J.; Kasim Ibraheem, I.; Ajel, A.R. A Novel Adaptive LMS Algorithm with Genetic Search Capabilities for System Identification of Adaptive FIR and IIR Filters. Information 2019, 10, 176.

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