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基于遗传算法的煤与瓦斯突出影响因素研究
供稿: 陶慧;祁佩棉 时间: 2018-12-27 次数:

作者:陶慧;祁佩棉

作者单位:河南理工大学电气工程与自动化学院中国矿业大学信息与电气工程学院;河北省电力局

摘要:针对采用BP神经网络对煤与瓦斯突出预测时的过学习现象,引入遗传算法对煤与瓦斯突出的影响因素进行选择,并建立了以筛选出的变量作为输入的优化BP网络预测模型.遗传算法中染色体采用二进制编码,个体适应度函数引入了惩罚函数,并对基本遗传算法的遗传操作算子进行了一定的改进,最后利用平煤八矿煤与瓦斯突出的实测样本,在MAT-LAB2009b环境中对上述算法进行仿真研究.结果表明,以遗传算法筛选出的变量作为输入建立的预测模型的输出结果的拟合效果变好,预测精度提高,建模时间缩短.

基金:国家自然科学基金项目资助(60974126);

关键词:煤与瓦斯突出;遗传算法;特征选择;神经网络;

DOI:10.16186/j.cnki.1673-9787.2011.03.014

分类号:TD713;TP183

Abstract:Genetic algorithm (GA) is applied to select main affecting factors of coal and gas outburst to solve the over-fitting problem of BP neural network (NN) in predicting coal and gas outburst, and a modified BP NN predictor is established, which input variables are the factors selected.In our GA, chromosome is a binary encoding, penalty function is introduced into fitness function, and genetic operators are improved.Finally, the method is studied using real samples of PingMei 8th mine in MATLAB2009b environment.The results demonstrate that fitting effect and prediction accuracy of the modified BP NN predictor is improved significantly and simulation time is shorter after predictor's valuables are optimized on GA.

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