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|conference=Proceedings of the 9th National Conference on Artificial Intelligence, Anaheim, CA, USA, July 14–19 |volume=2 |pages=762–767 |url=http://www.aaai.org/Papers/AAAI/1991/AAAI91-119.pdf }}</ref><ref>{{cite journal |last=Jang |first=J.-S.R. |year=1993 |title=ANFIS: adaptive-network-based fuzzy inference system |journal=IEEE Transactions on Systems, Man and Cybernetics |volume=23 |issue=3 |doi=10.1109/21.256541 }}</ref> Since it integrates both neural networks and [[fuzzy logic]] principles, it has potential to capture the benefits of both in a single [[:wikt:framework|framework]]. Its inference system corresponds to a set of fuzzy [[Conditional (programming)|IF–THEN rules]] that have learning capability to approximate nonlinear functions.<ref>{{Citation |chapter=Adaptation of Fuzzy Inference System Using Neural Learning |title=Fuzzy Systems Engineering: Theory and Practice |first=A. |last=Abraham |year=2005 |editor-first=Nadia |editor1-last=Nedjah |editor2-first=Luiza |editor2-last=de Macedo Mourelle |series=Studies in Fuzziness and Soft Computing |volume=181 |publisher=Springer Verlag |___location=Germany |doi=10.1007/11339366_3 |pages=53–83 }}</ref> Hence, ANFIS is considered to be a universal estimator.<ref>Jang, Sun, Mizutani (1997) – Neuro-Fuzzy and Soft Computing – Prentice Hall, pp 335–368, ISBN 0-13-261066-3</ref>. 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. <ref>{{cite paper|last=Tahmasebi|first=P. |title=A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation |year=2012 |
==References==
{{reflist}}
[[Category:Fuzzy logic]]
[[Category:Artificial neural networks]]
{{compu-AI-stub}}
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