Application of improved cerebella model articulation controller in forest fire recognition

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.