基于混沌免疫粒子群优化和广义回归神经网络的回采工作面瓦斯涌出量预测模型  

Gas emission prediction model of working face based on chaos immune particle swarm optimizations and generalized regression neural network

作　　者：王雨虹[1] 付华[1] 侯福营[1] 张洋[2] 

机构地区：[1]辽宁工程技术大学电气与控制工程学院,辽宁葫芦岛125105 [2]辽宁工程技术大学电子与信息工程学院,辽宁葫芦岛125105

出　　处：《计算机应用》2014年第11期3348-3352,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(51274118);辽宁省科技攻关项目(2011229011)

摘　　要：为提高回采工作面绝对瓦斯涌出量预测的精度和效率,提出了将混沌免疫粒子群优化(CIPSO)算法与广义回归神经网络(GRNN)相耦合的绝对瓦斯涌出量预测模型。该方法采用CIPSO对GRNN的光滑因子进行动态优化调整,减少了人为因素对GRNN网络输出结果的影响,并采用优化后的网络建立瓦斯涌出量预测模型。通过对某煤矿瓦斯涌出量数据的仿真实验结果表明:基于CIPSO-GRNN的回采工作面绝对瓦斯涌出量模型比BP神经网络、Elman网络预测模型具有更好的预测精度和收敛速度,证明了该方法的有效性和可行性。To improve the accuracy and efficiency of absolute gas emission prediction, a new algorithm based on Chaos Immune Particle Swarm Optimization（ CIPSO） and General Regression Neural Network（ GRNN） was proposed. In this algorithm, CIPSO was employed to dynamically optimize the smooth factor of GRNN to reduce the impact of artificial factors in GRNN model construction, and then the optimized network was adopted to establish gas emission prediction model. The simulation experiment results on gas emission data of a coal mine show that the model is of faster convergence and higher prediction accuracy than other prediction models based on BP and Elman neural network. It is proved that the proposed method is feasible and effective.

关 键 词：混沌免疫粒子群优化 广义回归神经网络 回采工作面 瓦斯涌出量 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]