An improved optimisation framework for fuzzy time-series prediction

IEEE International Conference on Fuzzy Systems

Volumn , Issue , 2013, Pages

  Ho, Duc Thang ^a   Garibaldi, Jonathan M ^a  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Fuzzy inference systems; Fuzzy logic; Fuzzy systems; Mackey Glass; Time series prediction

Indexed keywords

FUNCTION APPROXIMATION; FUZZY INFERENCE SYSTEMS; K-MEANS CLUSTERING ALGORITHM; MACKEY-GLASS; OPTIMISATION TECHNIQUES; PARALLEL GENETIC ALGORITHMS; TAKAGI SUGENO KANG FUZZY MODEL; TIME SERIES PREDICTION;

BENCHMARKING; FORECASTING; FREQUENCY HOPPING; FUZZY LOGIC; FUZZY SYSTEMS; GLASS; LEAST SQUARES APPROXIMATIONS; OPTIMIZATION;

ITERATIVE METHODS;

EID: 84887838692     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZ-IEEE.2013.6622545     Document Type: Conference Paper

References (21)

1. J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. Cambridge, MA, USA: MIT Press, 1992.
2. I. Rechenberg, Evolutionsstrategie: optimierung technischer systeme nach prinzipien der biologischen evolution. Frommann-Holzboog, 1973.
3. M. Almaraashi, R. John, S. Coupland, and A. Hopgood, "Time series forecasting using a TSK fuzzy system tuned with simulated annealing," in Fuzzy Systems (FUZZ), 2010 IEEE International Conference on, 2010, pp. 1-6.
4. J.-S. Jang, "ANFIS: Adaptive-Network-Based Fuzzy Inference System," IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, pp. 665-685, 1993.
5. W. Huang, L. Ding, and S.-K. Oh, "A Comparative Study of Space Search Algorithm and Particle Swarm Optimization in the Design of ANFIS-based Fuzzy Models," Majlesi Journal of Electrical Engineering, vol. 5, no. 1, 2011.
6. J.-N. Choi, S.-K. Oh, and H.-K. Kim, "Hybrid optimization of information granulation-based fuzzy radial basis function neural networks," International Journal of Intelligent Computing and Cybernetics, vol. 3, no. 4, pp. 593-610, 2010.
7. J. B. Macqueen, "Some methods for classification and analysis of multivariate observations," in Procedings of the Fifth Berkeley Symposium on Math, Statistics, and Probability, vol. 1. University of California Press, 1967, pp. 281-297.
8. P. Moscato, "On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms," California Institute of Technology, Tech. Rep. C3P Report 826, 1989.
9. M. Nowostawski and R. Poli, "Parallel genetic algorithm taxonomy," in Knowledge-Based Intelligent Information & Engineering Systems, 1999, pp. 88-92.
10. M. Mackey and L. Glass, "Oscillation and chaos in physiological control systems," Science, vol. 197, no. 4300, pp. 287-289, 1977.
11. S.-K. Oh, W. Pedrycz, and K.-J. Park, "Identification of fuzzy systems by means of genetic optimization and data granulation," Journal of Intelligent and Fuzzy Systems, vol. 18, no. 1, pp. 31-41, Jan. 2007.
12. D. Whitley, S. Rana, and R. B. Heckendorn, "The island model genetic algorithm: On separability, population size and convergence," Journal of Computing and Information Technology, vol. 7, pp. 33-47, 1998.
13. M. Russo, "Fugenesys-a fuzzy genetic neural system for fuzzy modeling," IEEE Transactions on Fuzzy Systems, vol. 6, pp. 373-388, 1998.
14. R. Penrose, "A generalized inverse for matrices," Mathematical Proceedings of the Cambridge Philosophical Society, vol. 51, no. 03, pp. 406-413, 1955.
15. D. M. Bates and D. G. Watts, Appendix 2. QR Decompositions Using Householder Transformations. John Wiley & Sons, Inc., 2008, pp. 286-289.
16. M. Russo, "Genetic fuzzy learning," IEEE Transactions on Evolutionary Computation, vol. 4, pp. 259-273, Sep 2000.
17. Y. Chen, B. Yang, A. Abraham, and L. Peng, "Automatic Design of Hierarchical Takagi-Sugeno Type Fuzzy Systems Using Evolutionary Algorithms," IEEE Transactions on Fuzzy Systems, vol. 15, pp. 385-397, 2007.
18. C. G. Coy and D. Kaur, "Improving evolutionary training for Sugeno Fuzzy Inference Systems using a Mutable Rule Base," in Conference of the North American Fuzzy Information Processing Society, July 2010, pp. 139-144.
19. J.-N. Choi, Y.-I. Lee, and S.-K. Oh, "Fuzzy Radial Basis Function Neural Networks with Information Granulation and Its Genetic Optimization," in International Symposium on Neural Networks, 2009, pp. 127-134.
20. Y. Chen, L. Peng, and A. Abraham, "Programming hierarchical ts fuzzy systems," in Evolving Fuzzy Systems, 2006 International Symposium on, 2006, pp. 157-162.
21. P. Angelov, "An approach for fuzzy rule-base adaptation using on-line clustering," International Journal of Approximate Reasoning, vol. 35, no. 3, pp. 275-289, 2004.