Coal Mine Gas Emission Prediction Based on IQPSO-BP Algorithm
CHENG Jiatang
AI Li
XIONG Yan
Abstract：Aiming at the nonlinear characteristics of gas emission in a coal mine working face, a method based on the improved quantum particle swarm optimized BP neural network ( IQPSO-BP) was proposed. In view of the limited ability to the traverse of the quantum particle swarm, chaotic sequences were used to initialize the initial angle position of particles. At the same time, the convex function was used to adjust the inertia weight and balance the global exploration and local development ability. Based on this, the weight and threshold parameters of BP neural network were optimized, and then the gas emission prediction model was established. The results showed that the IQPSO-BP algorithm had better generalization ability and higher prediction accuracy, and can be effectively used for the prediction of gas emission in a coal mine.
Keywords：gas emissionpredictionimproved quantum particle swarm optimization algorithm ( IQPSO )BP neural network
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 38-41 )