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System identification is an exceptionally expansive topic and of remarkablesignificance in the discipline of signal processing and communication. Our goalin this paper is to show how simple adaptive FIR and IIR filters can be used insystem modeling and demonstrating the application of adaptive systemidentification. The main objective of our research is to study the LMSalgorithm and its improvement by the genetic search approach, namely, LMS-GA,to search the multi-modal error surface of the IIR filter to avoid local minimaand finding the optimal weight vector when only measured or estimated data areavailable. Convergence analysis of the LMS algorithm in the case of colouredinput signal, i.e., correlated input signal is demonstrated on adaptive FIRfilter via power spectral density of the input signals and Fourier transform ofthe autocorrelation matrix of the input signal. Simulations have been carriedout on adaptive filtering of FIR and IIR filters and tested on white andcoloured input signals to validate the powerfulness of the genetic-based LMSalgorithm.

Adaptive System Identification Using LMS Algorithm Integrated with Evolutionary Computation