Autor: |
Zhu Wang, Haoran An, Xiong-Lin Luo |
Jazyk: |
angličtina |
Rok vydání: |
2019 |
Předmět: |
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Zdroj: |
Chemical Engineering Transactions, Vol 76 (2019) |
ISSN: |
2283-9216 |
Popis: |
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. |
Databáze: |
OpenAIRE |
Externí odkaz: |
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