Application of CLGA in Impulsion Pressure Predictio
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摘要: 煤体瓦斯涌出量的动态变化是一个复杂的非线性系统,传统的瓦斯监测方法准确率较低。针对该问题,文章提出了一种基于BP人工神经网络模型的瓦斯突出危险性预测控制方法。该方法运用BP人工神经网络预测模型对输入的多组样本进行训练学习、建立预测准则,并以此辨识瓦斯突出危险性类型。仿真结果表明,该方法有效解决了传统的瓦斯突出预测模型在事故预测中误差大、稳定性差的缺陷,提高了预测精度。Abstract: To solve problems of long time of network training,easy to fall into local minimum and easy to early maturity existed in traditional method of impulsion pressure prediction,the paper proposed a method using CLGA to predict impulsion pressure.It introduced basic steps using CLGA to optimize weight and threshold of BP neural network in details and made simulation experiment using Matlab7.1 to build network model on PC.The simulation result showed that the method can improve accuracy of impulsion pressure prediction effectively.
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Key words:
- impulsion pressure /
- prediction /
- BP neural network /
- GA /
- CLGA /
- prediction precisio
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