| 协同进化PSO算法在瓦斯含量预测中的应用研究 |
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| 引用本文： | 谢国民,康海潮,付华,何武林.协同进化PSO算法在瓦斯含量预测中的应用研究[J].压电与声光,2011,33(5). |
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| 作者姓名： | 谢国民 康海潮 付华 何武林 |
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| 作者单位： | 辽宁工程技术大学电气与控制工程学院,辽宁葫芦岛,125105 |
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| 基金项目： | 国家自然科学基金资助项目(50874059); 教育部博士点基金资助项目(200801470003); 辽宁省优秀人才基金资助项目(2007R18) |
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| 摘 要： | 针对影响瓦斯含量的各种因素之间的复杂非线性关系,提出了利用协同进化粒子群优化(HCPSO)算法优化带开关权值的神经网络,来实现煤层瓦斯含量预测.通过使用二进制值0和1来表示神经网络的节点间有无连接,并用二进制编码来调节神经网络的结构；同时使用协同进化粒子群优化(PSO)算法优化神经网络权值,来获得精度较高、结构精简的神经网络模型.实验结果表明,该方法有效提高了瓦斯含量预测的精度,为煤矿瓦斯预测提供了一种新方法.
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| 关 键 词： | 协同进化粒子群优化(HCPSO)算法 神经网络 瓦斯 预测 |
Study on Application of Co-operative Particle Swarm Optimization Algorithm to Forecasting In-situ Gas Content |
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| XIE Guomin,KANG Haichao,FU Hua,HE Wulin.Study on Application of Co-operative Particle Swarm Optimization Algorithm to Forecasting In-situ Gas Content[J].Piezoelectrics & Acoustooptics,2011,33(5). |
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| Authors: | XIE Guomin KANG Haichao FU Hua HE Wulin |
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| Affiliation: | XIE Guomin,KANG Haichao,FU Hua,HE Wulin(Faculty of Electrical and Control Engineering,Liaoning Project Technology University,Huludao 125105,China) |
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| Abstract: | Because there is a complex nonlinear relationship among the various factors of influencing the gas content,the hybrid-coding co-operative particle swarm optimization algorithm(HCPSO) to optimize the neural network with switch is proposed to forecast the seam gas content.By using the binary values 0 and 1 to indicate whether or not the neural network between nodes connects,and using the binary code to adjust the neural network structure,at the same time,using the co-evolution particle swarm optimization algo... |
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| Keywords: | cooperative PSO algorithms neural network gas forecast |
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