Clustering Input Signals Based Identification Algorithms for Two-Input Single-Output Models with Autoregressive Moving Average Noises

Khalid Abd El Mageed Hag ElAmin,Hassan Zargarzadeh

doi:10.1155/2020/2498487

Abstract

This study focused on the identification problems of two-input single-output system with moving average noises based on unsupervised learning methods applied to the input signals. The input signal to the autoregressive moving average model is proposed to be arriving from a source with continuous technical and environmental changes as two separate featured input signals. These two input signals were grouped in a number of clusters using the K-means clustering algorithm. The clustered input signals were supplied to the model in an orderly fashion from cluster-1 up to cluster-K. To ensure that the output signal can be best predicted from the input signal which in turn leads to selecting good enough model for its intended use, the magnitude-squared coherence (MSC) measure is applied to the input/output signals in the cases of clustered and nonclustered inputs, which indicates best correlation coefficient when measured with clustered inputs. From collected input-output signals, we deduce a K-means clustering based recursive least squares method for estimating the parameter of autoregressive moving average system. The simulation results indicate that the suggested method is effective.

Highlights

Most systems that have been inferred to prove many of the assumptions proposed in the area of system identification focused on single-input single-output systems [1,2,3,4]
The focus has been on studying system identification of multivariable systems in order to deal with an appropriate modelling and estimation of dynamic systems operating in industrial applications and process control [5,6,7]
Several methods and techniques addressed system identification problems in multivariable models as in [8,9,10,11,12]; the researchers in these studies depend on hierarchical identification principle that decomposes a multi-input system into two subsystems, one containing a parameter vector and the other containing a parameter matrix [10]. e proposed method in [13] was dependent on the concept of coupling identification to avoid matrix inversion in multivariable least squares in order to reduce computational time in the algorithms

Summary

Introduction

Most systems that have been inferred to prove many of the assumptions proposed in the area of system identification focused on single-input single-output systems [1,2,3,4]. E fundamental idea of the proposed method in this paper is to cluster only the input signals using K-means clustering algorithm without clustering the whole regressor vector. Based on this idea, we present a clustered input signals based recursive least squared algorithm. We present a clustered input signals based recursive least squared algorithm Given this recursive algorithm, we can produce more accurate parameter estimation compared to existing multivariable recursive estimation methods, for example, [14, 15].

The Model Description

The K-Means Clustering Algorithm

Illustrative Example