Adaptive neuro fuzzy inference system
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.For using the ANFIS in a more efficient and optimal way, one can use the best parameters obtained by genetic algorithm. [5]
- ^ 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. Vol. 2. pp. 762–767.
- ^ 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.
- ^ Abraham, A. (2005), "Adaptation of Fuzzy Inference System Using Neural Learning", in Nedjah, Nadia; de Macedo Mourelle, Luiza (eds.), Fuzzy Systems Engineering: Theory and Practice, Studies in Fuzziness and Soft Computing, vol. 181, Germany: Springer Verlag, pp. 53–83, doi:10.1007/11339366_3
- ^ Jang, Sun, Mizutani (1997) – Neuro-Fuzzy and Soft Computing – Prentice Hall, pp 335–368, ISBN 0-13-261066-3
- ^ Tahmasebi, P. (2012). "A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation" (PDF). 42: 18–27.
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