Haibo Liu1,*, Yujie Dong2, Fuzhong Wang1
Energy Engineering, Vol.118, No.3, pp. 679-689, 2021, DOI: 10.32604/EE.2021.014630
Abstract For the problems of nonlinearity, uncertainty and low prediction accuracy in the gas outburst prediction of coal
mining face, the least squares support vector machine (LSSVM) is proposed to establish the prediction model.
Firstly, considering the inertia coefficients as global parameters lacks the ability to improve the solution for
the traditional particle swarm optimization (PSO), an improved PSO (IPSO) algorithm is introduced to adjust
different inertia weights in updating the particle swarm and solve the fitness to stagnate. Secondly, the penalty
factor and kernel function parameter of LSSVM are searched automatically, and the regression accuracy and generalization performance is enhanced… More >
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