Nonlinear internal model control based on fuzzy rough granular Neural Networks
Zhang Tengfei, Tan Yaliang, Fumin Ma · 2014
A nonlinear system internal model control based on neural networks is studied in this paper. The fuzzy rough set theoretic techniques are used to knowledge extraction from the collected data. Extracted knowledge is then encoded into the network in the form of initial weights by granular computing. The Neural Network Controller is designed based on the proposed fuzzy rough granular neural networks. Meanwhile, the fuzzy neural network is used to design the Neural Network Model of nonlinear system. The identification and control of inverse model based on fuzzy rough granular neural network are researched in detail. The feasibility of the proposed control method is demonstrated by simulation analysis.