摘要
综合考虑了温度值和烟雾浓度值,利用神经网络的学习方法来构造模糊系统,根据输入输出样本来自动设计和调整模糊系统的设计参数,实现了模糊系统的自学习和自适应功能。仿真结果表明,该系统在公路隧道火灾报警中提高了火灾报警的准确性,减少了对报警器的依赖程度,并使火灾报警时间有所提前。
Temperature and desity of smoke are multiply considered in the system.Fuzzy system can be constructed by using learning method of neural network. The parameter of fuzzy system was designed and adapted automatically according to sample of input and output,then the function of fuzzy system's selflearning and selfadaptation was achieved. The simulation result shows that the system can increase the accuracy of firealarming,decrease tile reliance on annunciator and reduce the time of firealarming.
出处
《长安大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2003年第6期79-82,共4页
Journal of Chang’an University(Natural Science Edition)
