基于改进型T-S模糊RBF神经网络的红外火焰探测器识别算法

针对三波段红外火焰探测器中可能出现的单一非火焰波段通道的数据丢失、失真、饱和3种对火焰特征数据的强干扰情况,本文提出了一种改进型T-S(Takagi-Sugeno,高木-关野)模型RBF(Radial Basis Function,径向基函数)神经网络的火焰识别的鲁棒性融合算法.该算法通过聚类算法确定模型需要的模糊规则...

Recognition Algorithm for an Infrared Flame Detector Based on an Improved Takagi-Sugeno Fuzzy Radial Basis Function Neural Network

To address the data loss,distortion,and saturation of a single non-flame channel that may occur in a three-band infrared flame detector,a robust fusion algorithm for flame recognition based on a radial basis function(RBF)neural network entailing an improved Takagi-Sugeno(T-S)model is proposed in this paper.In this algorithm,the number of fuzzy rules required by the model is determined by a clustering algorithm.The membership degree of the feature component is added to the subsequent fuzzy polynomial to generate node output,and the weighted fuzzy node activation degree and feature characterization coefficient are defined to replace the Markov distance(fuzzy rule applicability)of the original model.Through the design of a three-band flame detector and routine and robustness experiments,it is shown that the proposed model significantly improves the number of nodes,convergence speed,accuracy,generalization ability,and robustness as compared with those of the traditional T-S model RBF neural network and genetic algorithm-back propagation models.