||J. S. R. Jang, C. T. Sun and E. Mizutani, Neuro-fuzzy and soft computing, Prentice Hall, NJ, USA, 1997.|
J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms, New York: Plenum, 1981.
M. A. Shoorehdeli, M. Teshnehlab, A. K. Sedigh and M. A. Khanesar, “Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods,” Applied Soft Computing, vol. 9, iss. 2, pp. 833-850, March 2009.
K. Erenturk, “ANFIS-based compensation algorithm for current-transformer saturation effects,” IEEE Transactions on Power Delivery, vol. 24, no. 1, January 2009.
Q. Yuan, C.Y. Dong and Q. Wang, “An adaptive fusion algorithm based on ANFIS for radar/infrared system,” Expert Systems with Applications, vol. 36, iss. 1, pp. 111-120, January 2009.
M. A. Boyacioglu and D. Avci, “An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange,” Expert Systems with Applications, vol. 37, iss. 12, pp. 7908-7912, December 2010.
L. A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, pp. 338-353, 1965.
R. Storn and K. Price, “Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, iss. 4, pp. 341-359, December 1997.
S. Das and P. N. Suganthan, “Differential evolution: a servey of the state-of-the-art” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp 4-31, February 2011.
W. K. Wong, M. Xia and W. C. Chu, “Adaptive neural network model for time-series forecasting,” European Journal of Operational Research, vol. 207, iss. 2, pp. 807-816, December 2010.
J. P. S. Catala ̃o, H. M. I. Pousinho, and V. M. F. Mendes, “Hybrid wavelet-PSO-ANFIS approach for short-term wind power forecasting in Portugal,” IEEE Transactions on Sustainable Energy, vol. 2, no. 1, January 2011.
H. Shayeghi and H. A. Shayanfar, “PSO based neuro-fuzzy controller for LFC design including communication time delays,” International Journal on “Technical and Physical Problems of Engineering” (IJTPE), vol. 2, no. 2, pp. 28-36, June 2010.
M. A. Shoorehdeli, M. Teshnehlab and A. K. Sedigh, “Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter,” Fuzzy Sets and Systems, vol. 160, iss. 7, pp. 922-948, April 2009.
Z. bingu ̈l and O. Karahan, “A fuzzy logic controller tuned with PSO for 2 DOF robot trajectory control,” Expert Systems With Applications, vol. 38, iss. 1, pp. 1017-1031, January 2011.
M. Eftekhari, S. D. Katebi, M. Karimi and A. H. Jahanmiri, “Eliciting transparent fuzzy model using differential evolution,” Applied Soft Computing, vol. 8, iss. 1, pp. 466-476, January 2008.
N. Chauhan, V. Ravi and D. K. Chandra, “Differential evolution trained wavelet neural networks: application to bankruptcy prediction in banks,” Expert Systems with Applications, vol. 36, iss. 4, pp. 7659-7665, May 2009.
A. K. Qin, V. L. Huang, and P. N. Suganthan, “Differential evolution algorithm with strategy adaptation for global numerical optimization,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 398-417, April 2009.
S. Rahnamayan, H. R. Tizhoosh, and M. M. A. Salama, “Opposition-based differential evolution,” IEEE Transactions on evolutionary computation, vol. 12, no. 1, pp. 64-79, February 2008.
J. Zhang, and A. C. Sanderson, “JADE: adaptive differential evolution with optional external archive” IEEE Transaction on Evolutionary Computation, vol. 13, no. 5, October 2009.
S. Das, A. Abraham, U. K. Chakraborty, and A. Konar, “Differential evolution using a neighborhood-based mutation operator,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 3, June 2009.
M. G. H. Ornran, A. P. Engelbrecht and A. Salman, “Bare bones differential evolution,” European Journal of Operational Research, vol. 196, iss. 1, pp. 128-139, July 2009.
C. Li and T. W. Chiang, “Function approximation with complex neuro-fuzzy system using complex fuzzy sets – a new approach,” New generation computing, vol. 29, no. 3, pp. 261-276, June 2011.
Z. Chen, S. Aghakhani, J. Man, and S. Dick, “ANCFIS: a neurofuzzy architecture employing complex fuzzy sets,” IEEE Transactions on Fuzzy Systems, vol. 19, no. 2, pp. 305-322, April 2011.
D. Graves and W. Pedrycz, “Fuzzy prediction architecture using recurrent neural networks,” Neurocomputing, vol. 72, iss. 7-9, pp. 1668-1678, March 2009.
L. Cao, “Support vector machines experts for time series forcasting,” Neurocomputing, vol. 51, pp. 321-339, 2003.
H. Tong and K. S. Lim, “Threshold autoregression, limit cycles and cyclical data,” Journal of the Royal Statistical Society. Series B (Methodological), vol. 42, no. 3, pp. 245-292, 1980.
A. S. Weigend, B. A. Huberman, and D. E. Rumelhart, “Predicting the future: a connectionist approach,” International Journal of Neural Systems (IJNS), vol. 1, iss. 3, pp. 193-209, 1990.
J. T. Tsai, J. H. Chou, and T. K. Liu, “Tuning the structure and parameters of a neural network by using hybrid Taguchi-Genetic algorithm,” IEEE Transactions on Newral Network, vol. 17, no. 1, January 2006.
N. Terui and H. K. van Dijk, “Combined forecasts from linear and nonlinear time series models,” International Journal of Forecasting, vol. 18, iss. 3, pp. 421-438, July-September 2002.
H. H. Chu, T. L. Chen, C. H. Cheng, and C. C. Huang, “Fuzzy dual-factor time-series for stock index forecasting,” Expert systems with applications, vol. 36, iss. 1, pp. 165-171, January 2009.
S. M. Chen, “Temperature prediction using fuzzy time series,” IEEE Transactions on cybernetics, vol. 30, no. 2, pp. 263-275, 2000.
K. Huarng, and T. H. K. Yu, “A type 2 fuzzy time-series model for stock index forecasting,” Physica A: statistical mechanics and its applications, vol. 353, page. 445-462, August 2005.
H. J. Teoh, T. L. Chen, C. H. Cheng and H. H. Chu, “A hybrid multi-order fuzzy time series for forecasting stock markets,” Expert systems with applications, vol. 36, iss. 4, pp. 7888-7897, May 2009.
S. M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy sets and systems, vol. 81, pp. 311-319, 1996.
H. K. Yu, “Weighted fuzzy time series models for TAIEX forecasting,” Physica A, vol. 349, pp. 609-624 2005.
R, L. Miklidiu ́, R. J. Machado, and R. P. Renter ́ia, “Time-series forecasting through wavelets transformation and a mixture of expert models,” Neurocomputing, vol. 28, iss. 1-3, pp. 145-156, October 1999.
J. Ma, “A method for multiple periodic factor prediction problems using complex fuzzy sets,” IEEE transactions on fuzzy systems, vol. 20, iss. 1, pp. 32-45, February 2012.
R. Storn and K. Price, “Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces,” ICSI, USA, Tech. Rep. TR-95-012, March 1995.
R. Storn, “On the usage of differential evolution for function optimization,” Fuzzy information processing society, 1996. NAFIPS. 1996 Biennial conference of the North American, pp. 519-523, June 1996.
A. Lendasse, F. Corona, J. Hao, N. Reyhani, and M. Verleysen, “Determination of the Mahalanobis matrix using nonparametric noise estimations,” ESANN’2006 proceedings – European Symposium on Artificial Neural Networks Bruges (Belgium), pp. 227-232, April 2006.
J. A. B. Tome ́ and J. P. Carvalho, “One step ahead prediction using fuzzy boolean neural networks,” EUSFLAT-LFA, pp. 500-505, 2005.
F. A. Gers, D. Eck, and J. Schmidhuber, “Applying LSTM to time series predictable through time-window approaches,” Conference Artificial Neural Network, pp. 669-676, 2001.
G. Bontempi, M. Birattari, and H. Bersini, “Local learning for iterated time series prediction,” Proc. ICML, pp. 32-38, 1999.
G. Dangelmayr, S. Gadaleta, D. Hundley, and M. Kirby, “Time series prediction by estimating Markov probabilities through topology preserving maps,” Proc. SPIE, vol. 3812, pp. 86-93, 1999.
T. Koskela, M. Varsta, J. Heikkonen, and K. Kaski, “Recurrent SOM with local linear models in time series prediction,” European Symposium on Artificial Neural Networks, pp. 167-172, 1998.
E. A. Wan, “Time series prediction by using a connectionist network with internal delay lines,” Time Series Prediction. Forecasting the Future and Understanding the Past, pp. 195-217, 1994.
A. S. Weigend and D. A. Nix, “Prediction with confidence intervals (local error bars),” University, Berlin, Berlin, Humboldt, Germany, 1994.
A. S. Weigend and N. A. Gershenfeld, “Results of the time series prediction competetion at the Santa Fe Institute,” IEEE International Conference on Neural Networks, vol. 3, pp. 1786-1793, 1993.
T. J. Cholewo and J. M. Zurada, “Sequential network construction for time series prediction,” IEEE ICNN, Houston, TX, pp.2034-2038, 1997.
M. Li, K. Mehrotra, C. Mohan and S. Ranka, “Sunspot numbers forecasting using neural networks,” Intelligent Control, 1990. Proceedings, 5th IEEE International Symposium on, vol. 1, pp. 524-529, September 1990.
H. H. Sargent III, “A prediction for the next sunspot Cycle,” Vehicular Technology Conference, 1978. 28th IEEE, pp. 490-496, March 1978.
M. R. Hassan, “Stock market forecasting using hidden Markov model: a new approach,” 5th international conference on Intelligent Systems Design and Applications, pp. 192-196, September 2005.