摘要
为了提高瓦斯浓度预测的精度和稳定性,提出了将遗传算法(GA)与BP神经网络结合的预测方法。利用BP神经网络能以任意精度逼近非线性函数的优点,结合遗传算法的全局搜索能力,优化神经网络权值和阈值,建立GA—BP混合算法模型预测瓦斯浓度。实验结果表明,GA—BP算法与BP神经网络相比,具有较高的预测精度和较强的稳定性。
In order to improve the accuracy and stability of the gas concentration prediction, a prediction method of combining genetic algorithm ( GA) and BP neural network was proposed. By using the advantage of BP neural network which can approach the nonlinear function with any accuracy, combining with the overall search ability of the genetic algorithm and optimizing the neural network weights and thresholds, a GA-BP hybrid algorithm model for gas concentration prediction was established. The experimental results show that GA-BP algorithm has higher prediction accuracy and stronger stability as compared with the BP neural network.
出处
《矿业安全与环保》
北大核心
2015年第2期56-60,共5页
Mining Safety & Environmental Protection
关键词
瓦斯浓度
BP神经网络
遗传算法
预测
gas concentration
BP neural network
genetic algorithms
prediction
