ANFIS modeling of Electro-Hydraulic Actuator system through physical modeling and FCM gap statistic in initial FIS determination

Abstract

The Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling technique has shown the ability to model nonlinear system at high accuracy. The initial Fuzzy Inference System (FIS) is required before ANFIS parameters' training. The generation of initial FIS requires the number of inputs, number of clusters and cluster centers to be determined. Instead of determining the above parameters by heuristic approach, the number and variable of inputs of an Electro-Hydraulic Actuator (EHA) system are determined through mathematical derivation of the system. The number of clusters for each input is determined via fuzzy gap statistic, which applies Fuzzy C-means (FCM) instead of k-means of gap statistic in calculating the within cluster dispersion. FCM clustering also provides the center of each cluster identified. Information of the input variables and cluster numbers as well as cluster centers are used to obtain initial FIS. ANFIS hybrid training algorithm is used to train the initial FIS for parameters. The result shows that the model obtained using ANFIS approach can estimate the EHA's system response at high precision.