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A Spatiotemporal Fuzzy Modeling Approach Combining Automatic Clustering and Hierarchical Extreme Learning Machines for Distributed Parameter Systems

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Gepubliceerd in:Mathematics vol. 13, no. 3 (2025), p. 364
Hoofdauteur: Zhou, Gang
Andere auteurs: Zhang, Xianxia, Wang, Tangchen, Wang, Bing
Gepubliceerd in:
MDPI AG
Onderwerpen:
Temperature distribution
Accuracy
Partial differential equations
Genetic algorithms
Noise prediction
Modelling
Clustering
Temperature
Fuzzy systems
Sensors
Chemical vapor deposition
Basis functions
Silicon wafers
Distributed parameter systems
Methods
Batteries
Machine learning
Boundary conditions
Radiation
Coupling
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Samenvatting:Modeling distributed parameter systems (DPSs) is challenging due to their strong nonlinearity and spatiotemporal coupling. In this study, a three-dimensional fuzzy modeling method combining genetic algorithm (GA)-based automatic clustering and hierarchical extreme learning machine (HELM) is proposed for DPS modeling. The method utilizes GA-based automatic clustering to learn the premise part of 3D fuzzy rules, while HELM is employed to learn spatial basis functions and construct a complete fuzzy rule base. This approach effectively captures the spatiotemporal coupling characteristics of the system and mitigates the information loss commonly observed in dimensionality reduction in traditional fuzzy modeling methods. Through experimental verification, the proposed method is successfully applied to a rapid thermal chemical vapor deposition system. The experimental results demonstrate that the method can accurately predict temperature distribution and maintain good robustness under noise and disturbances.
ISSN:2227-7390
DOI:10.3390/math13030364
Bron:Engineering Database