Implementation of LMS algorithm for system identification

Abstract

An adaptive filter is defined as a digital filter that has the capability of self adjusting its transfer function under the control of some optimizing algorithms. Most common optimizing algorithms are Least Mean Square (LMS) and Recursive Least Square (RLS). Although RLS algorithm perform superior to LMS algorithm, it has very high computational complexity so not useful in most of the practical scenario. So most feasible choice of the adaptive filtering algorithm is the LMS algorithm including its various variants. The LMS algorithm uses transversal FIR filter as underlying digital filter. This paper is based on implementation and optimization of LMS algorithm for the application of unknown system identification.

Identifikasi Parameter Adaptif Motor Arus Searah Penguat Terpisah Menggunakan Algoritma LMS

“…Penelitian [8], menggunakan tiga metode yaitu algoritma LMS, normalized least mean square (NLMS), dan recursive least square (RLS) untuk mengidentifikasi sistem yang tidak diketahui. Sementara itu, implementasi identifikasi sistem dengan menggunakan algoritma LMS dasar dilakukan oleh [9], dengan menggunakan dua laju konvergensi yang berbeda yaitu 0,05 dan 0,025. Kemudian, hasil simulasi dengan jelas menunjukkan bahwa bobot estimasi sama dengan bobot yang sebenarnya.…”

“…Kemudian, hasil simulasi dengan jelas menunjukkan bahwa bobot estimasi sama dengan bobot yang sebenarnya. Di samping itu, [10] juga melakukan identifikasi parameter sistem yang tidak diketahui menggunakan algoritma LMS. Plant diasumsikan sebagai model linear yang kemudian diberi masukan sinyal acak, kemudian setelah disimulasikan dengan dua laju konvergensi yaitu 0,3 dan 0,15, diperoleh hasil penelitian bahwa bobot yang di estimasi sesuai dengan bobot yang sebenarnya.…”

Adaptive method to predict and track unknown system behaviors using RLS and LMS algorithms

“…Bernard Widrow of Stanford University invented the LMS algorithm in 1959. It follows the steepest descent algorithm in which the filter is adjusted according to the current time error [7]. Mukhopadhyay S. et al developed the LMS algorithm to solve the problem of missing data.…”

Design of Adaptive Planar Microstrip Patch Array Operating at 28 GHz for 5G Smart Mobile System

“…This can be achieved using a simple Finite Impulse Response (FIR) tapped delay line filter. To get the optimum beamformer the FIR filter could be changing the weights adaptively and reduced the Mean Square Error (MSE) between the required and existing signals [10]. The common algorithm that is used in the present work is the Least Mean Square (LMS) method.…”