This paper presents the architecture and learning procedure underlying ANFIS (Adaptive-Network-based Fuzzy Inference System), a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In our simulation, we employ the ANFIS architecture to model nonlinear functions, identify nonlinear components on-linely in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificail neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested.
In REF , Jang presents an adaptive neural based Fuzzy inference system (ANFIS) that was used to refine the Fuzzy if-then rules, being in this case a microscopic optimization.
ANFIS: Adaptive-Network-Based Fuzzy Inference System
IEEE Transactions on Systems, Man, and Cybernetics
Computer Science