Infrared flame detection based on a self-organizing TS-type fuzzy neural network

Abstract The radial basis function neural network (RBF-NN) integrating Takagi-Sugeno (TS) fuzzy model has been widely used in pattern recognition and intelligence system for its relatively simple structure, good local approximating capability, interpretably and function equivalence. In this paper, we propose a new scheme to generate the variable structure of the model by adding, merging or deleting corresponding nodes (fuzzy rules). Then an adaptive learning algorithm is developed to overcome the local minimum by adjusting parameters just with the gradient descent. To improve the robustness of the model, a bias of firing strength is proposed to suppress the outputs of the outliers as well as the recognition of the faults in infrared flame detection. Finally, experimental data obtained from a developed triple-channel infrared flame detector are used to demonstrate the performance of the improved self-organizing radial basis function fuzzy neural network (ISO-TS-RBF) compared with SO-TS-RBF in [1], TS-RBF in [2] and TS-type fuzzy neural network with particle swarm optimization (PSO-TS-RBF) in [3]. The comparison results show that the model we proposed has the best accuracy, generalization ability and robustness that it can well recognize the outliers caused by the real faults occurred in the infrared flame detection procedure.

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