基于改进粒子群优化BP神经网络的火灾预警方法

Fire warning method based on improved particle swarm optimization BP neural network

针对传统仓库火灾预警系统易受外界环境因素干扰,误报漏报率高的问题,采用一种基于改进粒子群优化BP神经网络的火灾预警方法,增强仓库消防系统的安全性和智能性,为传统检测系统增加一层保险。在标准粒子群优化BP神经网络的基础上,引入Tent映射,选取新的权重更新函数,改进粒子群随机搜索能力,加速神经网络收敛,输出期望结果。由Matlab软件仿真实验结果分析可知,改进后的方法相较于标准粒子群优化方法,收敛速度和准确率均显著提升,证明了该方法的优越性。

Aiming at the problem that traditional warehouse fire warning system is easy to be disturbed by external environmental factors and the false alarm rate is high,a fire warning method based on improved particle swarm optimization BP neural network is adopted to enhance the safety and intelligence of warehouse fire warning system and add a layer of insurance for the traditional detection system.Based on the standard BP neural network optimized by particle swarm optimization,Tent mapping and new weight updating function are introduced to improve the random searching ability of particle swarm optimization,accelerate the convergence of neural network,and output the desired results.According to the analysis of simulation experiment results with Matlab,compared with the standard particle swarm optimization method,the convergence speed and accuracy of the improved method are significantly improved,which proves the superiority of the method.