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.^[5][6]

References

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.