Robust fusion algorithm based on RBF neural network with TS fuzzy model and its application to infrared flame detection problem

Abstract

A robust fusion algorithm based on Radial Basis Function (RBF) neural network with Takagi–Sugeno (TS) fuzzy model is proposed in view of the data loss, data distortion or signal saturation which is usually occurred in the process of infrared flame detecting with multiple sensors. To initialize the model, the traditional K-means clustering algorithm is used to obtain the number of the fuzzy rules and the center of the membership function. Compared with the traditional RBF neural network with TS fuzzy model, the output of the node in the proposed model is constructed taking into account the membership degree of the feature components in each item of the output polynomial of the hidden layer nodes in consequent fuzzy network. A new weighted activation degree (WAD) is defined to calculate the firing strength (i.e., fuzzy rule applicability) of the fuzzy node instead of the commonly used Mahalanobis distance. The feature representation coefficients used in the above WAD fully consider the variant representation degree of different features in different fuzzy clusters, thus the developed method can deal with the abnormal outputs of the fuzzy rules caused by the variation of the feature components of the raw data obtained from the complex industrial environments. The robustness of the proposed approach is validated with experimental data obtained from a developed triple-channel infrared flame detector and the experiment results show that the convergence rate, accuracy and generalization ability of the proposed method are improved compared with the traditional RBF neural network with TS fuzzy model in Qiao et al. (2014) and the GA-BP (Genetic Algorithm-Back Propagation) model in Wang et al. (2016). In particular, the required number of the hidden layer nodes in the proposed approach is the least among the aforementioned methods.