Adaptive neuro fuzzy inference system

From Wikipedia, the free encyclopedia
Jump to navigation Jump to search

An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s.[1][2] Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework. Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate nonlinear functions.[3] Hence, ANFIS is considered to be a universal estimator.[4] For using the ANFIS in a more efficient and optimal way, one can use the best parameters obtained by genetic algorithm.[5][6]


  1. ^ Jang, Jyh-Shing R (1991). Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm (PDF). Proceedings of the 9th National Conference on Artificial Intelligence, Anaheim, CA, USA, July 14–19. 2. pp. 762–767.
  2. ^ Jang, J.-S.R. (1993). "ANFIS: adaptive-network-based fuzzy inference system". IEEE Transactions on Systems, Man and Cybernetics. 23 (3). doi:10.1109/21.256541.
  3. ^ Abraham, A. (2005), "Adaptation of Fuzzy Inference System Using Neural Learning", in Nedjah, Nadia; de Macedo Mourelle, Luiza, Fuzzy Systems Engineering: Theory and Practice, Studies in Fuzziness and Soft Computing, 181, Germany: Springer Verlag, pp. 53–83, doi:10.1007/11339366_3
  4. ^ Jang, Sun, Mizutani (1997) – Neuro-Fuzzy and Soft Computing – Prentice Hall, pp 335–368, ISBN 0-13-261066-3
  5. ^ Tahmasebi, P. (2012). "A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation" (PDF). Computers & Geosciences. 42: 18–27.
  6. ^ Tahmasebi, P. (2010). "Comparison of optimized neural network with fuzzy logic for ore grade estimation" (PDF). Australian Journal of Basic and Applied Sciences. 4: 764–772.