This paper presents a new effective partitioning technique of linearly transformed input space in Adaptive Network based Fuzzy Inference System (ANFIS). The ANFIS is the fuzzy system with a hybrid parameter learning method, which is composed of a gradient and a least square method. The input space can be partitioned flexibly using new modeling inputs, which are the weighted linear combination of the original inputs by the proposed input partitioning technique, thus, the parameter learning time and the modeling error of ANFIS can be reduced. The simulation result illustrates the effectiveness of the proposed technique.

This paper presents a new fuzzy dynamic output feedback controller design technique for the Takagi Sugeno (T-S) fuzzy model with unknown-but-bounded time-varying modeling error. It is shown that the quadratic stabilization problem of the T-S fuzzy modeled system can be converted into an H control problem of the scaled polytopic Linear Parameter Varying (LPV) system. Then, a controller satisfying a prescribed H performance is designed for the stabilization of the T-S fuzzy modeled system.