Enhancing the performance of the fuzzy system approach to prediction

Proceedings - 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008

Volumn 1, Issue , 2008, Pages 259-265

  Thi, Thanh Nguyen ^a   Peterson, Jim ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DIESEL ENGINES; FINANCIAL DATA PROCESSING; FUZZY SETS; FUZZY SYSTEMS; GENETIC ALGORITHMS; KNOWLEDGE BASED SYSTEMS; LEARNING ALGORITHMS;

FINANCIAL TIME SERIES; FUZZY SET; SYSTEM APPROACHES; TIME-SERIES DATUM;

FUZZY LOGIC;

EID: 58149139969     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FSKD.2008.198     Document Type: Conference Paper

References (28)

1. A.E. Gaweda, Optimal data-driven rule extraction using adaptive fuzzy-neural models, Ph.D. Dissertation, University of Louisville, Kentucky, USA, 2002.
2. A.N. Refenes, Y. Bentz, D.W. Bunn, A.N. Burgess, and A.D. Zapranis, "Financial Time Series Modeling with Discounted Least Squares Backpropagation", Neurocomputing, vol. 14, no. 2, 1997, pp. 123-138.
3. B. Kosko, "Fuzzy systems as universal approximators", IEEE Transactions on Computers, vol. 43, no. 11, 1994, pp. 1329-1333.
4. B. Kosko, "Optimal fuzzy rules cover extrema". International Journal of Intelligent Systems, vol. 10, no. 2, 1995, pp. 249-255.
5. B. Kosko, "Global stability of generalized additive fuzzy systems", IEEE Transactions on Systems, Man, and Cybernetics, vol. 28, no. 3, 1998, pp. 441-452.
6. D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley, Massachusetts, USA, 1989.
7. D. Gao, Y. Kinouchi, K. Ito, and X. Zhao, "Neural networks for event extraction from time series: a back propagation algorithm approach", Future Generation Computer Systems, vol. 21, no. 7, 2005, pp. 1096-1105.
8. E.S. Gardner, Jr. and E. McKenzie, "Model Identification in Exponential Smoothing", Journal of the Operational Research Society, vol. 39, no. 9, 1988, pp. 863-867.
9. G.D. Magoulas, M.N. Vrahatis, and G.S. Androulakis, "Improving the Convergence of the Backpropagation Algorithm Using Learning Rate Adaptation Methods", Neural Computation, vol. 11, 1999, pp. 1769-1796.
10. G.J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, Upper Saddle River, NJ, USA, 1995.
11. th International Conference on Pattern Recognition, vol. 4, 1996, pp. 586-588.
12. J.A. Dickerson and B. Kosko, "Fuzzy function approximation with supervised ellipsoidal learning", World Congress on Neural Networks, vol. 2, 1993, pp. 9-13.
13. J.A. Dickerson and B. Kosko, "Fuzzy function approximation with ellipsoidal rules", IEEE Transactions Systems, Man, and Cybernetics, vol. 26, no. 4, 1996, pp. 542-560.
14. J.E. Fowler, Adaptive Vector Quantization for the Coding of Nonstationary Sources, SPANN Laboratory Technical Report TR-95-05, The Ohio State University, 1995.
15. J.T. Yao and C.L. Tan, "A Study on Training Criteria for Financial Time Series Forecasting", Proceedings of International Conference on Neural Information Processing, 2001, pp. 772-777.
16. K. Alsabti, S. Ranka, and V. Singh, "An Efficient K-Means Clustering Algorithm", IPPS/SPDP Workshop on High Performance Data Mining, IEEE Computer Society Press, 1998.
17. K. Kohara, "Neural Multivariate Prediction Using Event-Knowledge and Selective Presentation Learning", IEEE International Conference on Neural Networks, vol. 1, 1995, pp. 359-364.
18. K. Kohara, T. Ishikawa, Y. Fukuhara and Y. Nakamura, "Stock Price Prediction Using Prior Knowledge and Neural Networks", Intelligent Systems in Accounting, Finance and Management, vol. 6, no. 11, 1997, pp. 11-22.
19. L. Yao, K.S. Weng, and C.D. Huang, "A Fuzzy Classifier with Evolution Design of Ellipsoidal Decision Regions", Proceedings of World Academy of Science, Engineering and Technology, vol. 1, 2005, pp. 449-453.
20. N. Qian, "On the momentum term in gradient descent learning algorithms", Neural networks, vol. 12, 1999, pp. 145-151.
21. NIST/SEMATECH, Engineering Statistics e-Handbook, http://www.itl.nist.gov/div898/handbook/, 2006.
22. th IEEE International Conference on Fuzzy Systems, vol. 2, 1996, pp. 1237-1243.
23. S. Mitaim and B. Kosko, "Adaptive joint fuzzy sets for function approximation", Proceeding of IEEE International Conference on Neural Networks, vol. 1, 1997, pp. 537-542.
24. S. Mitaim and B. Kosko, "The shape of fuzzy sets in adaptive function approximation", IEEE Transactions on Fuzzy Systems, vol. 9, no. 4, 2001, pp. 637-656.
25. S.C. Newton, S. Pemmaraju, and S. Mitra, "Adaptive fuzzy leader clustering of complex data sets in pattern recognition", IEEE Transactions on Neural Networks, vol. 3, no. 5, 1992, pp. 794-800.
26. T. Kanungo, D.M. Mount, N.S. Netanyahu, C.D. Piatko, R. Silverman, and A.Y. Wu, "An Efficient k-Means Clustering Algorithm: Analysis and Implementation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, 2002, pp. 881-892.
27. T.T. Nguyen and S.T. Do, Applying Standard Additive Model for economic prediction problems, Bachelor Dissertation, Faculty of Information Technology, University of Technology, Vietnam National University Ho Chi Minh City, Vietnam, 2006.
28. X.B. Yan, T. Lu, Y.J. Li, and G.B. Cui, "Research on event prediction in time-series data", Proceedings of International Conference on Machine Learning and Cybernetics, vol. 5, 2004, pp. 2874-2878.