ANFIS: adaptive-network-based fuzzy inference system

1993/01/01 by J.-S.R. Jang, Jyh‐Shing Roger Jang · 16,218 citations
Computer Science · Engineering · #Adaptive neuro fuzzy inference system #Artificial intelligence #Artificial neural network #Computer science #Control engineering #Data mining #Engineering #Fault Detection and Control Systems #Fuzzy Logic and Control Systems #Fuzzy control system #Fuzzy inference system #Fuzzy logic #Inference system #Machine learning #Neural Networks and Applications #Neuro-fuzzy #Nonlinear system

paper · doi:10.1109/21.256541

published in IEEE Transactions on Systems Man and Cybernetics 23(3), 665-685 (Institute of Electrical and Electronics Engineers)

openalex publication_date 1993/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is 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 the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial 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.>

Citations

• Outline of a New Approach to the Analysis of Complex Systems and Decision Processes
• Fuzzy logic in control systems: fuzzy logic controller. I
• Fuzzy identification of systems and its applications to modeling and control

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