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Optimization of Extreme Learning Machine and Application in Decoupling of the Cross-direction Basis Weight System for Papermaking
Received:February 20, 2023  
DOI：10.11980/j.issn.0254-508X.2023.12.019
Key Words:paper basis weight  static decoupling  extreme learning machine  optimization
Fund Project:国家自然科学基金项目（62073206）。
Author NameAffiliationPostcode
SHEN Yunzhu College of Electrical and Control Engineering， Shaanxi University of Science & Technology， Xi’an， Shaanxi Province， 710021 710021
TANG Wei* College of Electrical and Control Engineering， Shaanxi University of Science & Technology， Xi’an， Shaanxi Province， 710021 710021
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Abstract:In this paper， the extreme learning machine （ELM） was improved based on strange nonchaotic optimization （SNO） and used to solve the coupling problem of cross-direction （CD） basis weight system. Firstly， the SNO based on a piecewise logistic map was used to optimize the randomly generated weights and thresholds between the input layer and the hidden layer， which solved the disadvantage of insufficient optimization for ELM. Then， SNO extreme leaming machine （SNOELM） decouplers were designed to decouple the multivariable system. Finally， it was compared with the improved extreme learning machine， whale optimization extreme learning machine （WOELM） and particle swarm optimization extreme leaming machine （PSOELM）. Simulation results demonstrated that the SNOELM decoupling method had better optimization ability than ELM and had higher decoupling accuracy and faster decoupling speed than WOELM and PSOELM.
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