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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] For using the ANFIS in a more efficient and optimal way, one can use the best parameters obtained by genetic algorithm.[5][6] It has uses in intelligent situational aware energy management system.[7]

ANFIS architecture

It is possibile to identify two parts in the network structure, namely premise and consequence parts. In more details, the architecture is composed by five layers. The first layer takes the input values and determines the membership functions belonging to them. It is commonly called fuzzification layer. The membership degrees of each function are computed by using the premise parameter set, namely {a,b,c}. The second layer is responsible of generating the firing strengths for the rules. Due to its task, the second layer is denoted as "rule layer". The role of the third layer is to normalize the computed firing strengths, by diving each value for the total firing strength. The fourth layer takes as input the normalized values and the consequence parameter set {p,q,r}. The values returned by this layer are the defuzzificated ones and those values are passed to the last layer to return the final output.[8]

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. Vol. 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): 665–685. doi:10.1109/21.256541.
  3. ^ 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, CiteSeerX 10.1.1.161.6135, doi:10.1007/11339366_3, ISBN 978-3-540-25322-8
  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". Computers & Geosciences. 42: 18–27. doi:10.1016/j.cageo.2012.02.004. PMC 4268588. PMID 25540468.
  6. ^ Tahmasebi, P. (2010). "Comparison of optimized neural network with fuzzy logic for ore grade estimation". Australian Journal of Basic and Applied Sciences. 4: 764–772.
  7. ^ https://ieeexplore.ieee.org/document/8387835
  8. ^ Karaboga, Dervis; Kaya, Ebubekir (2018). "Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey". Artificial Intelligence Review. doi:10.1007/s10462-017-9610-2. ISSN 0269-2821.


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