优化神经网络在多传感器火灾探测中的应用

针对BP神经网络在拟合过程中探测精度低、容易陷入局部最优的问题，提出一种基于遗传算法（GA）和模拟退火算法（SA）共同改进的BP神经网络模型，该网络模型可以有效提高火灾识别准确率，同时避免网络过拟合现象，使预测结果跳出局部最优从而达到全局最优。首先，通过GA改进隐藏层结构部分，然后通过SA改进连接权重部分，最后利用优化后的GA-SA-BP模型对火灾实验数据进行信息融合实现火灾探测。实验研究表明，对比单一BP神经网络，经GA和SA改进后的BP神经网络能够有效改善网络拟合能力，并提升火灾探测精度至98.91%。

Application of optimized neural network in multi-sensor fire detection

On the problems of BP neural network in the process of fitting, such as low detection precision, easy to fall into local optimum, an optimized BP neural network model based on genetic algorithm(GA) and simulated annealing algorithm(SA) was developed. The model can significantly improve the recognition accuracy, while avoid the network over fitting phenomenon, and make the forecast results jump out of local optimal so as to achieve the global optimal. Firstly, the hidden layer structure was improved by GA, and then the connection weight was improved by SA. Finally, the optimized GA-SA-BP model was used for information fusion of fire experimental data to realize fire detection. Experimental results show that compared with the single BP neural network, the BP neural network improved by GA and SA can effectively improve the fitting ability of the network, and improve the accuracy of fire detection to 98.91%.