Improved Intelligent Identification of Hammerstein-Wiener Systems by Particle Swarm Optimization and K-Means Clustering
This paper considers the robust identification of Hammerstein-Wiener systems in the presence of Gaussian or non-Gaussian noises. An improved intelligent identification scheme is exploited by combining particle swarm optimization (PSO) and K-means clustering. The proposed scheme has strong ability to...
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2019-10-01
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Series: | Chemical Engineering Transactions |
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doaj-f74b908511074c91a16d71744a1d2cfb2021-02-16T20:58:23ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162019-10-017610.3303/CET1976111Improved Intelligent Identification of Hammerstein-Wiener Systems by Particle Swarm Optimization and K-Means ClusteringZhu WangHaoran AnXiong-Lin LuoThis paper considers the robust identification of Hammerstein-Wiener systems in the presence of Gaussian or non-Gaussian noises. An improved intelligent identification scheme is exploited by combining particle swarm optimization (PSO) and K-means clustering. The proposed scheme has strong ability to keep the balance between exploration and exploitation. Its procedure is about “global particle swarm optimization search — K-means clustering — local particle swarm optimization search”. The proposed scheme can identify the parameters of the general Hammerstein-Wiener system with dead zone and saturation characteristics, and obtain a more accurate model for the actual production process. Relative to other improved particle swarm optimization methods, the accuracy of parameter estimation is improved by nearly 53 % at data length L=2000. In particular, the method can better model nonlinear dynamics and facilitate the precise implementation of control in chemical production.https://www.cetjournal.it/index.php/cet/article/view/10562 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhu Wang Haoran An Xiong-Lin Luo |
spellingShingle |
Zhu Wang Haoran An Xiong-Lin Luo Improved Intelligent Identification of Hammerstein-Wiener Systems by Particle Swarm Optimization and K-Means Clustering Chemical Engineering Transactions |
author_facet |
Zhu Wang Haoran An Xiong-Lin Luo |
author_sort |
Zhu Wang |
title |
Improved Intelligent Identification of Hammerstein-Wiener Systems by Particle Swarm Optimization and K-Means Clustering |
title_short |
Improved Intelligent Identification of Hammerstein-Wiener Systems by Particle Swarm Optimization and K-Means Clustering |
title_full |
Improved Intelligent Identification of Hammerstein-Wiener Systems by Particle Swarm Optimization and K-Means Clustering |
title_fullStr |
Improved Intelligent Identification of Hammerstein-Wiener Systems by Particle Swarm Optimization and K-Means Clustering |
title_full_unstemmed |
Improved Intelligent Identification of Hammerstein-Wiener Systems by Particle Swarm Optimization and K-Means Clustering |
title_sort |
improved intelligent identification of hammerstein-wiener systems by particle swarm optimization and k-means clustering |
publisher |
AIDIC Servizi S.r.l. |
series |
Chemical Engineering Transactions |
issn |
2283-9216 |
publishDate |
2019-10-01 |
description |
This paper considers the robust identification of Hammerstein-Wiener systems in the presence of Gaussian or non-Gaussian noises. An improved intelligent identification scheme is exploited by combining particle swarm optimization (PSO) and K-means clustering. The proposed scheme has strong ability to keep the balance between exploration and exploitation. Its procedure is about “global particle swarm optimization search — K-means clustering — local particle swarm optimization search”. The proposed scheme can identify the parameters of the general Hammerstein-Wiener system with dead zone and saturation characteristics, and obtain a more accurate model for the actual production process. Relative to other improved particle swarm optimization methods, the accuracy of parameter estimation is improved by nearly 53 % at data length L=2000. In particular, the method can better model nonlinear dynamics and facilitate the precise implementation of control in chemical production. |
url |
https://www.cetjournal.it/index.php/cet/article/view/10562 |
work_keys_str_mv |
AT zhuwang improvedintelligentidentificationofhammersteinwienersystemsbyparticleswarmoptimizationandkmeansclustering AT haoranan improvedintelligentidentificationofhammersteinwienersystemsbyparticleswarmoptimizationandkmeansclustering AT xionglinluo improvedintelligentidentificationofhammersteinwienersystemsbyparticleswarmoptimizationandkmeansclustering |
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1724266669375750144 |