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  

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

作　　者：冯宏伟 刘媛媛[2,3] 温子腾 谭勇 FENG Hongwei;LIU Yuanyuan;WEN Ziteng;TAN Yong(Wuxi Institute of Technology,Wuxi 214122,China;Wuxi Professional College of Science and Technology,Wuxi 214028,China;School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China)

机构地区：[1]无锡职业技术学院,江苏无锡214121 [2]无锡科技职业学院,江苏无锡214028 [3]江南大学物联网工程学院,江苏无锡214122

出　　处：《红外技术》2021年第1期37-43,共7页Infrared Technology

基　　金：国家自然科学基金项目(61374047)。

摘　　要：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数,在模糊后件多项式中加入特征分量隶属度生成节点输出,同时定义了加权模糊节点激活度和特征表征系数代替了原先模型的马氏距离(模糊规则适用度)。通过设计三波段火焰探测器并进行了常规及鲁棒性实验,实验数据证实,改进型模型在隐含层所需节点数、收敛速度、精度、泛化能力、鲁棒性上较传统T-S模型的RBF神经网络模型、GA(Genetic Algorithm,遗传算法)-BP(Back Propagation,反向传播)模型都有明显的提升。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.

关 键 词：红外火焰探测器 改进型T-S RBF神经网络 识别算法 

分 类 号：TN215[电子电信—物理电子学]