An incorporate genetic algorithm based back propagation neural network model for coal and gas outburst intensity prediction

机译：基于整合遗传算法的煤与瓦斯突出强度预测的BP神经网络模型

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The traditional GABP model used in complex coal and gas outbursts prediction, which trains the back-propagation neural networks (BPNN) by Genetic Algorithm (GA), is provided with some limitations, such as massive time-consuming, optimal stop condition of GA pretreatment indeterminacy, independency and complex task of great importance. To overcome these problems, a new method of coal and gas outbursts intensity prediction by Incorporate Genetic Algorithm Based Back Propagation Neural Network (IGABP) is applied to determine parameters of BPNN automatically and propose an efficient GA which reduces its iterative computation time for enhancing the training capacity of BPNN. First, improved GA is based on single population model among continuous generation model and used the modified self-adapted crossover rate, crossover strategy, self-adapted stop criterion, as well as special survival condition. Second, BP operator is introduced into the evolution of GA operations, improving the standard GA optimization of random search and self-guiding optimization searching. To show the validity of the proposed method, we compare it with traditional GABP and IGABP using a dataset. The results show that the IGABP model can effectively overcome the inadequacies of the traditional model, its operating efficiency and forecast performance are improved significantly.

机译：传统的GABP模型用于复杂的煤与瓦斯突出预测中，通过遗传算法（GA）训练反向传播神经网络（BPNN），但存在一些局限性，例如大量时间，GA预处理的最佳停止条件不确定性，独立性和复杂的任务非常重要。为了克服这些问题，采用基于遗传算法的反向传播神经网络（IGABP）预测煤与瓦斯突出强度的新方法，自动确定BPNN的参数，并提出了一种有效的遗传算法来减少迭代计算时间，以增强训练效果。 BPNN的容量。首先，改进的遗传算法基于连续生成模型中的单一种群模型，并使用了经过修改的自适应交叉率，交叉策略，自适应停止准则以及特殊的生存条件。其次，将BP算子引入遗传算法的进化过程中，改进了随机搜索和自导寻优的标准遗传算法优化。为了显示该方法的有效性，我们使用数据集将其与传统的GABP和IGABP进行了比较。结果表明，IGABP模型可以有效克服传统模型的不足，其运行效率和预测性能都有明显提高。

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来源
《The 6th international conference on mining science amp; technology (ICMST2009)》|2009年|p.1-8|共8页
会议地点 Xuzhou(CN);Xuzhou(CN)
作者
Yang Min; Wang Yun-jia; Cheng Yuan-ping;
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作者单位

School of Environmental Science Spatial Informatics, China University of Mining Technology, Xuzhou 221116, China Jiangsu Key Laboratory for Resources Environmental Information Engineering, China University of Mining Technology, Xuzhou 221116, China;

School of Environmental Science Spatial Informatics, China University of Mining Technology, Xuzhou 221116, China Jiangsu Key Laboratory for Resources Environmental Information Engineering, China University of Mining Technology, Xuzhou 221116, China;

School of Safety Engineering, China University of Mining Technology, Xuzhou, Jiangsu 221116, China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类矿业工程;
关键词
coal and gas outburst; outburst intensity prediction; incorporate genetic algorithm based back propagation neural network; improved model; BP operator;

机译：煤与瓦斯突出；突出强度预测；基于遗传算法的BP神经网络；改进模型； BP算子;

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机译：基于整合遗传算法的煤与瓦斯突出强度预测的BP神经网络模型

An incorporate genetic algorithm based back propagation neural network model for coal and gas outburst intensity prediction

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