Adaptive neuro fuzzy inference system

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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]

References[edit]

  1. ^ Jang, Jyh-Shing R (1991). "Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm". 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