T-S Fuzzy Model Identification with Growing and Pruning Rules for Nonlinear Systems
规则可生长与修剪的非线性系统T-S模糊模型辨识

Offline rule extraction for the T-S fuzzy systems usually gives a fixed number of fuzzy rules,which make it a bot- tleneck for revealing the complexity of nonlinear systems.Thus, due to a growing and pruning strategy of the neural network, in this paper the fuzzy rules are extracted from real-time data and their number is adjusted online by the impact degree of one local model,such that the rules vary with the system dy- namically and more precisely reflect the character of nonlinear systems.Furthermore,the accuracy of the T-S model is guaran- teed by the parameter learning based on a competitive extended Kalman filter(EKF).The entire algorithm presents a completely online identification of the T-S model and gains a structural and parameter adaptability.An example for CSTR identification il- lustrates its good performance.