Enhanced Grasshopper Optimization Algorithm For Modeling Multivariable Systems With Svr
PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC)(2019)
摘要
This paper proposes an enhanced grasshopper optimization algorithm (EGOA) for modelling a multivariable system with SVR. In the EGOA, the coevolution strategy and Gaussian mutation operator are employed to improve the search ability. The results with some test functions show the good performance of EOGA. The EGOA is adopted to model the binary distillation column using SVR. The simulation results demonstrate that the proposed modeling method has small modeling error and can reflect the dynamic characteristics of the multivariable plant.
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关键词
Enhanced grasshopper optimization algorithm (EGOA), Support vector regression (SVR), Multivariable system modelling
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