Partitioning of linearly transformed input space in adaptive network basedfuzzy inference system

This paper presents a new effective partitioning technique of linearly tran sformed input space in Adaptive Network based Fuzzy Inference System (ANFIS ). Tho ANFIS is thr fuzzy system with a hybrid parameter learning method, w hich is composed of a gradient and a least square method. The input space c an be partitioned flexibly using new modeling inputs, which are the weighte d linear combination of the original inputs by the proposed input partition ing technique, thus, the parameter Learning time and the modeling error of ANFIS can Lu reduced, The simulation result illustrates the effectiveness o f the proposed technique.