PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah

System Identification, a discipline for constructing models from dynamic systems, consist of three major steps: structure selection, parameter estimation and model validation. The parameter estimation step is concerned with the estimation of model parameters once the structure is known. Typically, p...

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Main Author:	Abdullah, Siti Muniroh
Format:	Thesis
Language:	English
Published:	2017
Subjects:	Electric apparatus and materials. Electric circuits. Electric networks Microelectronics
Online Access:	http://ir.uitm.edu.my/id/eprint/21667/ http://ir.uitm.edu.my/id/eprint/21667/1/TM_SITI%20MUNIROH%20ABDULLAH%20EE%2017_5.pdf

id	uitm-21667
recordtype	eprints
spelling	uitm-216672018-09-25T08:40:39Z http://ir.uitm.edu.my/id/eprint/21667/ PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah Abdullah, Siti Muniroh Electric apparatus and materials. Electric circuits. Electric networks Microelectronics System Identification, a discipline for constructing models from dynamic systems, consist of three major steps: structure selection, parameter estimation and model validation. The parameter estimation step is concerned with the estimation of model parameters once the structure is known. Typically, parameter estimation is performed using various types of Least Squares (LS) algorithms due to its stable and efficient numerical computation. However, LS methods suffers from oversmoothing properties (such as in curve fitting) and high sensitivity to outliers as error squaring significantly increases in magnitude. Additionally, when the range of data increases, it makes the nonlinear processes become more difficult. In this thesis, the Particle Swarm Optimization (PSO) is proposed for parameter estimation of a Nonlinear Autoregressive Moving Average with Exogeneous Inputs (NARMAX) and its derivatives ((NARX) and (NARMA)) of three datasets, which are Direct Current Motor (DCM), Flexible Robot Arm (FRA) and Mackey Glass (MG) system. PSO is a swarm-based search algorithm perform a stochastic search to explore the search space. Due to its stochastic nature, the algorithm does not inherit the numerical problems posed by the LLS algorithms. The proposed method is compared with three established conventional Linear Least Squares (LLS) solution methods : Normal Equation (NE), QR factorization (QR) and Singular Value Decomposition (SVD). Results suggest that the PSO algorithm is viable alternative to other established algorithms for LLS parameter estimation. In DCM NARX experiment, LLS is outperform in term of criterion fitness while PSO outperform in correlation violation values. For model fit test (MSE and R-squared) both methods perform similar. For FRA NARMAX, PSO outperform than LLS in term of criterion fitness, MSE and correlation violation while in R-squared analysis both methods perform similar. In the last experiment MG NARMA, LLS outperform than PSO in MSE value, criterion fitness and R -squared analysis while in term of correlation violation, PSO outperform than LLS. Additionally, the PSO algorithm was found to improve the correlation tests (reduction in correlation violation 22.22% in DCM NARX, 1.89% in FRA NARMAX and 10.46% in MG NARMA experiment) relative to the LLS algorithms. 2017 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/21667/1/TM_SITI%20MUNIROH%20ABDULLAH%20EE%2017_5.pdf Abdullah, Siti Muniroh (2017) PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah. Masters thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Electric apparatus and materials. Electric circuits. Electric networks Microelectronics
spellingShingle	Electric apparatus and materials. Electric circuits. Electric networks Microelectronics Abdullah, Siti Muniroh PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah
description	System Identification, a discipline for constructing models from dynamic systems, consist of three major steps: structure selection, parameter estimation and model validation. The parameter estimation step is concerned with the estimation of model parameters once the structure is known. Typically, parameter estimation is performed using various types of Least Squares (LS) algorithms due to its stable and efficient numerical computation. However, LS methods suffers from oversmoothing properties (such as in curve fitting) and high sensitivity to outliers as error squaring significantly increases in magnitude. Additionally, when the range of data increases, it makes the nonlinear processes become more difficult. In this thesis, the Particle Swarm Optimization (PSO) is proposed for parameter estimation of a Nonlinear Autoregressive Moving Average with Exogeneous Inputs (NARMAX) and its derivatives ((NARX) and (NARMA)) of three datasets, which are Direct Current Motor (DCM), Flexible Robot Arm (FRA) and Mackey Glass (MG) system. PSO is a swarm-based search algorithm perform a stochastic search to explore the search space. Due to its stochastic nature, the algorithm does not inherit the numerical problems posed by the LLS algorithms. The proposed method is compared with three established conventional Linear Least Squares (LLS) solution methods : Normal Equation (NE), QR factorization (QR) and Singular Value Decomposition (SVD). Results suggest that the PSO algorithm is viable alternative to other established algorithms for LLS parameter estimation. In DCM NARX experiment, LLS is outperform in term of criterion fitness while PSO outperform in correlation violation values. For model fit test (MSE and R-squared) both methods perform similar. For FRA NARMAX, PSO outperform than LLS in term of criterion fitness, MSE and correlation violation while in R-squared analysis both methods perform similar. In the last experiment MG NARMA, LLS outperform than PSO in MSE value, criterion fitness and R -squared analysis while in term of correlation violation, PSO outperform than LLS. Additionally, the PSO algorithm was found to improve the correlation tests (reduction in correlation violation 22.22% in DCM NARX, 1.89% in FRA NARMAX and 10.46% in MG NARMA experiment) relative to the LLS algorithms.
format	Thesis
author	Abdullah, Siti Muniroh
author_facet	Abdullah, Siti Muniroh
author_sort	Abdullah, Siti Muniroh
title	PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah
title_short	PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah
title_full	PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah
title_fullStr	PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah
title_full_unstemmed	PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah
title_sort	pso and linear ls for parameter estimation of narmax/narma/narx models for non-linear data / siti muniroh abdullah
publishDate	2017
url	http://ir.uitm.edu.my/id/eprint/21667/ http://ir.uitm.edu.my/id/eprint/21667/1/TM_SITI%20MUNIROH%20ABDULLAH%20EE%2017_5.pdf
first_indexed	2023-09-18T23:07:04Z
last_indexed	2023-09-18T23:07:04Z
_version_	1777418554149175296

PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah

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