BP神经网络技术在交通工具火灾预警中的应用

通过对交通工具火灾成因机理以及现有典型交通工具火灾实例的分析研究,建立了预警评价指标体系。根据非线性理论和模式识别原理以及交通工具火灾的特点,采用基于BP神经网络的智能灾害诊断方法,对交通工具火灾发生的可能性和危险性进行评估和预测。研究表明:BP神经网络方法是解决非线性系统问题的一种有效方法,与传统的预警方法相比,该方法具有速度快、效率高、可信度好、自学能力强等特点。采用BP网络进行交通工具火灾预警时,只需输入影响交通工具火灾发生的相关指标因素,网络便可在较短的时间内得出可靠的预警结果。

Application of BP Neural Network in the Fore-warning of Vehicle Fires

Through analysis of fire causes and research on typical fire cases of vehicle, a fore-warning evaluation index system is established, which will conduct real time supervision of the potential danger and development of vehicle fires. According to the non-linear and model recognition theory, the fire risk of vehicle is evaluated by adopting intelligent disaster diagnostic method based on BP Neural Network. Compared with traditional fore-warning methods, the method has such virtues as rapid speed, high efficiency and strong capability of self-learning, and it is one of the effective methods to solve non-linear system. The case study shows that reliable evaluation result can be obtained in shorter time as long as related indexes influencing vehicle fire risk are input into BP neural network.