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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.<ref>{{cite paper|last=Tahmasebi|first=P. |title=A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation |year=2012 |journal=Computers & Geosciences |volume=42 |pages=18–27 |url=http://www.sciencedirect.com/science/article/pii/S0098300412000398/pdfft?md5=cb070472e2eaa79fed8a2edf48943992&pid=1-s2.0-S0098300412000398-main.pdf }}</ref><ref>{{cite paper|last=Tahmasebi|first=P. |title=Comparison of optimized neural network with fuzzy logic for ore grade estimation |year=2010 |journal=Australian Journal of Basic and Applied Sciences |volume=4 |pages=764-772 |url=https://www.researchgate.net/profile/Ardeshir_Hezarkhani/publication/266881168_Comparison_of_Optimized_Neural_Network_with_Fuzzy_Logic_for_Ore_Grade_Estimation/links/5677da9308ae502c99d52f94.pdf }}</ref>
ANFIS has been applied on the active control of piezocomposite beams and plates
<ref>AD Muradova, GK Tairidis, GT Stavroulakis, Adaptive Neuro-Fuzzy vibration control of a smart plate. Numerical Algebra, Control and Optimization 7, 251-271, 2017 https://aimsciences.org/article/doi/10.3934/naco.2017017</ref>
==References==
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