AHP-RBF神经网络在煤矿安全风险评价中的应用

煤矿生产的复杂性导致煤矿事故具有动态、随机、模糊的特性,而且影响煤矿安全风险等级的指标众多,风险等级与风险指标之间呈现出复杂的非线性关系。传统的BP神经网络评价方法的准确率低,本文提出一套新的煤矿安全风险评价方法。首先建立煤矿安全风险指标体系,然后利用AHP确定各风险指标权重,并对其进行重要性排序,最后将RBF神经网络作为评价工具,建立基于AHP-RBF神经网络的煤矿安全风险评价模型。通过案例分析表明该模型能够科学有效的对煤矿安全状况进行评价。

The application of AHP-RBF neural network in coal mine safety risk evaluation

The complexity of the production in coal mine cause of the dynamic characteristics, fuzziness and randomness of coal mine accidents, and impact of coal mine safety risk grade indexes, the complex nonlinear relationship between index and risk level leads to low accuracy of traditional BP neural network evaluation method. In order to improve the accuracy of coal mine safety risk assessment, this paper puts forward a new method of coal mine safety risk assessment. First based on the method of literature investigation and expert evaluation index system of coal mine safety risk, and then use analytic hierarchy process (AHP) to determine the index weight, the index for sorting, finally put the RBF neural network as assessment tool, building the coal mine safety risk assessment model based on the AHP of RBF neural network. Through the instance data analysis, the results show that the effectiveness of the proposed evaluation model.