信息与控制2012，Vol.41Issue(6)：741-746,759,7.DOI:10.3724/SP.J.1219.2012.00741
基于量子粒子群优化的小波神经网络预测模型
A Wavelet Neural Network Prediction Model Based on Quantum Particle Swarm Optimization
摘要
Abstract
In order to solve the multifactor mine gas emission prediction problem, a wavelet neural network prediction model is proposed based on quantum particle swarm optimization (QPSO) algorithm. The proposed model utilizes the feature extraction capability of wavelet neural network (WNN), and applies the QPSO algorithm to determining the optimal initial parameters of the WNN. Simulation results show that the optimized WNN prediction model has the advantages of fast convergence, good fitting ability, high prediction accuracy and the ability to give the only prediction result. Additionally, by comparing the simulation data in the experiments, the reason of instability during the network prediction, the contradiction between the network training and prediction accuracy are analyzed. Then the key decision factors and evaluation methods are given for the prediction ability of the proposed model.关键词
量子粒子群优化/小波神经网络/瓦斯涌出量/优化/预测Key words
quantum particle swarm optimization/ wavelet neural network/ gas emission/ optimization/ prediction分类
信息技术与安全科学引用本文复制引用
潘玉民,张晓宇,张全柱,薛鹏骞..基于量子粒子群优化的小波神经网络预测模型[J].信息与控制,2012,41(6):741-746,759,7.基金项目
河北省教育厅科研基金项目(Z2006439). （Z2006439）
