Gray Wolf Optimization and Least Square Estimatation As A New Learning Algorithm For Interval Type-II ANFIS
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Abstract
Gray Wolfe Optimization (GWO) is one of the meta-heuristic method and it is a popular technique in Many engineering and economic applications. GWO and Least Square Estimatation (LSE) are used to optimize the antecedents and consequents parameters of interval type-2 ANFIS respectively. We are checking the new learning algorithm by using the interval type-2 ANFIS in prediction of Mackey-Glass time series and the results were very encouraging compared to other algorithms.
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References
1.Zadeh, L.A., Information and control. Fuzzy sets, 1965. 8(3): p. 338-353.
2.Zadeh, L.A., The concept of a linguistic variable and its application to approximate reasoning—II. Information sciences, 1975. 8(4): p. 301-357.
3.Suparta, W. and K.M. Alhasa, Modeling of tropospheric delays using ANFIS. 2016: Springer.
4.Mohamad, A.A., N.K. Hussein, and I.A. Morbat, Prediction of Fuzzy Sunspot Time Series By Using RBFANN. Tikrit Journal of Pure Science, 2017. 22(11).
5.Jang, J.-S., ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 1993. 23(3): p. 665-685.
6.Negnevitsky, M., Artificial intelligence: a guide to intelligent systems. 2005: Pearson Education.
7.Jang, J.-S.R., C.-T. Sun, and E. Mizutani, Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on automatic control, 1997. 42(10): p. 1482-1484.
8.Mirjalili, S., S.M. Mirjalili, and A. Lewis, Grey wolf optimizer. Advances in engineering software, 2014. 69: p. 46-61.
9.Almaraashi, M. and R. John. Tuning of type-2 fuzzy systems by simulated annealing to predict time series. in Proceedings of the world Congress on Engineering. 2011.
10.Hassan, S., et al. Hybrid model for the training of interval type-2 fuzzy logic system. in International Conference on Neural Information Processing. 2015. Springer.
11.Eyoh, I., R. John, and G. De Maere, Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction. 2016.
12.Soto, J., P. Melin, and O. Castillo. A new approach for time series prediction using ensembles of ANFIS models with interval type-2 and type-1 fuzzy integrators. in Computational Intelligence for Financial Engineering & Economics (CIFEr), 2013 IEEE Conference on. 2013. IEEE.
13.Castillo, O., et al. Type-2 fuzzy logic: theory and applications. in Granular Computing, 2007. GRC 2007. IEEE International Conference on. 2007. IEEE.