ADAPTIVE FILTER ANALYSIS FOR SYSTEM IDENTIFICATION USING VARIOUS ADAPTIVE ALGORITHMS

摘要

This paper includes the analysis of various adaptive algorithms such as LMS, NLMS, Leaky LMS, Sign-Sign, Sign-error and RLS for system identification. The problem of obtaining a model of system from input and output measurements is called the system identification problem. Using adaptive filter we can find the mathematical model of unknown system based on the input and output measurement. And analyze different parameter of algorithm such as order of filter, step size, leakage factor, normalized step size and forgetting factor. It has been found that RLS faster than other, but for practical consideration LMS is better. Complexity of LMS is less as compare to RLS because of less floating point operation. As the order increases magnitude response of adaptive filter is nearly equal to the response of unknown system and mean square error also reduced.