改进CMAC在森林火焰识别中的应用

Application of improved cerebella model articulation controller in forest fire recognition

由于传统火情识别存在的缺陷,提出一种基于双曲正割函数的变步长最小均方(LMS)算法的小脑模型神经网络(CMAC)森林火焰识别系统。通过分析火焰初期的一些静态和动态特性,对森林火焰进行初步识别。并在利用最优阈值搜寻法对图像进行分割处理的基础上,提取出相应的特征向量,作为改进CMAC的输入,利用神经网络进行森林火焰检测与识别。实验仿真表明,能对火焰进行准确、有效的判别。

Concerning the defects of traditional fire recognition, a forest fire recognition system of Cerebella Model Articulation Controller （CMAC） network, which was based on variable step Least Mean Square （LMS） algorithm of hyperbolic secant, was presented. Through analyzing some initial static and dynamic characteristics, forest fire was preliminarily identified. And on the basis of image segmentation using the optimal threshold search method, the corresponding eigenvectors were extracted as the input of the improved CMAC network to detect and identify forest fire. The simulation results show that the improved method can accurately and efficiently identify flame.