Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter

This paper proposes three steps of improvements for identification of the nonlinear dynamic system, which exploits the concept of a state-space based time domain Volterra model. The first step is combining the forward and backward estimator in the original Volterra model; the second step is reformul...

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Main Author: ., Edwar Yazid, Mohd Shahir Liew, Setyamartana Parman, Velluruzhati
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.12098 /
Published: 2015
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access: http://www.sciencedirect.com/science/article/pii/S1568494615003956
http://eprints.utp.edu.my/12098/
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spelling utp-eprints.120982017-04-06T02:29:34Z Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter ., Edwar Yazid, Mohd Shahir Liew, Setyamartana Parman, Velluruzhati TA Engineering (General). Civil engineering (General) This paper proposes three steps of improvements for identification of the nonlinear dynamic system, which exploits the concept of a state-space based time domain Volterra model. The first step is combining the forward and backward estimator in the original Volterra model; the second step is reformulating the Volterra model into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the kernel coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC). The applicability of the proposed methods is tested in three simulated data and one experimental data. The results show that Volterra model with PSO–KS is preferable for fast identification process, while ABC–KS method is preferable for accurate identification process. However, in some cases, as the iteration number increases the result of PSO–KS method is comparable with ABC–KS method. 2015-10 Article PeerReviewed http://www.sciencedirect.com/science/article/pii/S1568494615003956 ., Edwar Yazid, Mohd Shahir Liew, Setyamartana Parman, Velluruzhati (2015) Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter. Edwar Yazid, Mohd Shahir Liew, Setyamartana Parman, Velluruzhatil John Kurian . (Submitted) http://eprints.utp.edu.my/12098/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
., Edwar Yazid, Mohd Shahir Liew, Setyamartana Parman, Velluruzhati
Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter
description This paper proposes three steps of improvements for identification of the nonlinear dynamic system, which exploits the concept of a state-space based time domain Volterra model. The first step is combining the forward and backward estimator in the original Volterra model; the second step is reformulating the Volterra model into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the kernel coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC). The applicability of the proposed methods is tested in three simulated data and one experimental data. The results show that Volterra model with PSO–KS is preferable for fast identification process, while ABC–KS method is preferable for accurate identification process. However, in some cases, as the iteration number increases the result of PSO–KS method is comparable with ABC–KS method.
format Article
author ., Edwar Yazid, Mohd Shahir Liew, Setyamartana Parman, Velluruzhati
author_sort ., Edwar Yazid, Mohd Shahir Liew, Setyamartana Parman, Velluruzhati
title Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter
title_short Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter
title_full Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter
title_fullStr Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter
title_full_unstemmed Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter
title_sort improving the modeling capacity of volterra model using evolutionary computing methods based on kalman smoother adaptive filter
publishDate 2015
url http://www.sciencedirect.com/science/article/pii/S1568494615003956
http://eprints.utp.edu.my/12098/
_version_ 1741196142537867264
score 11.62408

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