Study of System Identification method Using Adaptive Filter and Neural Network

Abstract

System Identification is an important way of investigating the world around with proper understanding.This paper deals with the System Identification of a given Black box in which the inputs and outputs are known.It is a method of deriving a mathematical model of a pre-defined part the world,using observations. We aim at reducing the error of the system which is also the cost function. We deal with various methods of training the system according to the given inputs and outputs. System identification include mathematical tools and algorithms that build dynamic models from measured data. The learning paradigm for a given system allows a system to emulate the functions of the environment it is embedded in.We come across a number of neural networks and how it functions in the process.Simulation results exhibits least mean square algorithm using an adaptive filter system modelled and simulated in the MATLAB/Simulink environment.