2018/05/30 by Ibraheem Kasim Ibraheem, Ibraheem, Ibraheem Kasim
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Applications (stat.AP) #Control Systems and Identification #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1806.01782
openalex publication_date 2018/05/30 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
System identification is an exceptionally expansive topic and of remarkable\nsignificance in the discipline of signal processing and communication. Our goal\nin this paper is to show how simple adaptive FIR and IIR filters can be used in\nsystem modeling and demonstrating the application of adaptive system\nidentification. The main objective of our research is to study the LMS\nalgorithm and its improvement by the genetic search approach, namely, LMS-GA,\nto search the multi-modal error surface of the IIR filter to avoid local minima\nand finding the optimal weight vector when only measured or estimated data are\navailable. Convergence analysis of the LMS algorithm in the case of coloured\ninput signal, i.e., correlated input signal is demonstrated on adaptive FIR\nfilter via power spectral density of the input signals and Fourier transform of\nthe autocorrelation matrix of the input signal. Simulations have been carried\nout on adaptive filtering of FIR and IIR filters and tested on white and\ncoloured input signals to validate the powerfulness of the genetic-based LMS\nalgorithm.\n