Research on Optimization Algorithm of Key Parameters of Soft Measurement Model for Straw Fermentation Process
The performance of the least squares support vector machine soft measurement model for the key parameters in the process of straw fermentation to produce fuel ethanol depends on the selection of the model regularization parameters and kernel parameters.In order to address this problem,this paper proposes an optimization algorithm for the dung beetle population based on the chaotic and triangular wandering strategies,which utilizes chaotic mapping to initialize the population and improve the diversity of the population,and introduces a triangular wandering strategy into the position updating strategy,which can be shown to be better compared to the original optimization algorithm in terms of convergence speed and searching accuracy by simulation tests of the standard function set.The simulation test of the standard function set can show that the improved optimization algorithm has better convergence speed and optimization accuracy than the original dung beetle population optimization algorithm,which can provide optimization ideas for the subsequent parameter optimization of the soft measurement model.
least squares support vector machinechaostriangle wandering strategydung beetle population optimization
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