摘要
在综合研究了各种算法的基础上,将粗集理论和BP神经网络结合,充分利用了粗集算法能够去除冗余信息,BP神经网络能够精确加快收敛速度的优点。利用具体网络建立一个突出预测机制,并利用该预测机制对矿井瓦斯突出情况进行模拟预测。实际应用效果表明:采用基于MATLAB神经网络工具箱的BP网络模型,能克服一般BP网络收敛较慢的缺点,能加快收敛速度。实验结果表明:基于粗集-BP神经网络的预测模型可靠,收敛速度快,预测精度高,效果良好。
Based on the comprehensive study of the various algorithms,with the rough set theory and the BP neural network, taking advantage of rough set method can remove redundant information, BP neural network can accurately accelerate the convergence speed advantages. Prominent use of a specific network prediction mechanism, and use this prediction of mine gas outburst mechanism to predict the situation. Practical application demonstrates that the modified BP prediction model based on the MATLAB neural network toolbox can overcome the disadvantages of eonstringency and has fast convergence speed and good prediction accuracy. The experimental result reveals that based on rough set-BP neural network prediction model is reliable, fast convergence and high accuracy, good effect.
出处
《仪表技术与传感器》
CSCD
北大核心
2011年第11期100-103,共4页
Instrument Technique and Sensor
基金
国家自然科学基金资助项目(50874059)
