| 基于粒子群优化神经网络的煤层顶板导水裂隙带高度预测研究 |
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| 引用本文： | 潘晖.基于粒子群优化神经网络的煤层顶板导水裂隙带高度预测研究[J].科技广场,2009(3). |
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| 作者姓名： | 潘晖 |
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| 作者单位： | 中国矿业大学资源与地球科学学院,江苏,徐州,221008 |
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| 摘 要： | 阐述了运用粒子群优化人工神经网络建立煤层顶板导水裂隙带高度预测模型的思路与方法。利用粒子群优化神经网络模型的权值和阈值，克服了神经网络容易收敛到局部最小值，以及收敛速度慢的缺点。煤层导水裂隙带高度预测实例表明，该方法不仅能更快地收敛于最优解，且预测精度有明显的提高。
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| 关 键 词： | 导水裂隙带 粒子群优化算法 神经网络 |
Prediction of Water-flowing Fractured Zone's Height in Roof of Coal Based on Neural Networks Trained by Particle Swarm Optimization |
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| Pan Hui.Prediction of Water-flowing Fractured Zone's Height in Roof of Coal Based on Neural Networks Trained by Particle Swarm Optimization[J].Science Mosaic,2009(3). |
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| Authors: | Pan Hui |
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| Institution: | College of Resources and Earth Science;China University of Mining Technology;Jiangsu Xuzhou 221008 |
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| Abstract: | The paper introduces the theory and method of application of artificial neural network trained by particle swarm optimization in establishing forecasting model of the Water-flowing Fractured Zone's Height in roof of coal.PSO algorithm is applied to optimize weights of BP neural network,and it overcomes both slow convergence speed and local optimization of the neural network.Finally an example was used to verify the proposed methodology can speedily converge to the optional solution and had better generaliza... |
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| Keywords: | Water-flowing Fractured Zone Particle Swarm Optimization Neural Network |
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