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一种用于火灾识别的高斯函数动态神经网络分类方法
引用本文:闫晓玲,王黎明,卜乐平.一种用于火灾识别的高斯函数动态神经网络分类方法[J].武汉理工大学学报(交通科学与工程版),2012,36(2):337-340.
作者姓名:闫晓玲  王黎明  卜乐平
作者单位:海军工程大学电气与信息工程学院 武汉430033
摘    要:在火灾识别过程中,模式类的统计特性通常是未知的或者无法估计的,这类决策问题最好直接通过训练,生成所需的判别函数来处理.文中构建了高斯动态神经网络G-DNN结构,给出了G-DNN的理论分析和具体的算法步骤.对于网络学习的收敛速度非常重要网络参数σ,μ,ω,τ初值设定问题,充分利用参数与输入特征值、输出值和中间值分别相关的思想,引入参数预定义的方法,实验证明这种方法可以使网络学习较快收敛.

关 键 词:火灾识别  高斯函数  动态神经网络  模糊规则  网络结构

A Gauss Dynamic Nerve Network Classification Method for Fire Recognition
Yan Xiaoling , Wang Lingming , Pu Leping.A Gauss Dynamic Nerve Network Classification Method for Fire Recognition[J].journal of wuhan university of technology(transportation science&engineering),2012,36(2):337-340.
Authors:Yan Xiaoling  Wang Lingming  Pu Leping
Institution:Yan Xiaoling Wang Lingming Pu Leping (Electric and Informational College,Naval Engineering University,Wuhan 430033,China)
Abstract:During fire recognition processing,pattern′s statistical characteristic is often unknown or inestimable.The best method to solve this problem is direct training by producing criterion functions.In this article,Gauss Dynamic Nerve Network(DNN) is imported into fire recognition.At first,Gauss fuzzy dynamic nerve network(G-DNN) structure is set up and its theory and specific analyse steps are also present.In network,the parameters′ initial value such as σ,μ,ω,τ is very important for the study convergence speed.The article make the best of idea that the parameters are separately correlative with the input eigenvalues,output values and middle values,it applies parameter predefined method and this method is confirmed to make the network have a better convergence speed capability.
Keywords:fire recognition  Gauss function  DNN  fuzzy rule  network structure
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