Sensitivity Analysis of Fuzzy Inference Neural Network and the Application in Band Selection

Sensitivity analysis is used to estimate the effective variable (set) in a system. It plays an important role in model simplification, quality assurance of models and codes, identification of crucial regions in the parameter space and so on. Many interesting results have been obtained in the sensitivity analysis of traditional feed forward neural network. But the related work hasn´t been done in fuzzy neural network (FNN). In this paper, we choose fuzzy inference neural network (FINN), which has the advantages of representing the uncertain information and higher approximation capability, to study the sensitivity analysis of FNN. We firstly propose spFINN which is the simplification of a FINN introduced by Takatoshi Nishina and present a corresponding training algorithm. The simplification can make the sensitivity analysis process easier without reducing the approximation capability. Then a procedure called FINNSI is proposed to evaluate the sensitivity of input variable of spFINN. As the kernel idea of FINNSI, the way of separation and integration of the sensitivity of neurons is introduced because the sensitivity of neurons is transferred from the last layer (output layer) to the first layer (input layer). The procedure in FINNSI can be easily generalized to the sensitivity analysis of other fuzzy systems or networks. Finally, we discuss the application of FINNSI in band selection. The sensitive bands can be found to help the subsequent steps of spectral data processing such as object recognition and mineral classification.