TNFIS: Tree-Based Neural Fuzzy Inference System

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2008, Pages 398-405

  Cheu, Eng Yeow ^a   Quek, Hiok Chai ^a   Ng, See Kiong ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION THEORY; DECISION TREES; FUZZY INFERENCE; FUZZY SYSTEMS; INFERENCE ENGINES; LEARNING ALGORITHMS; LEARNING SYSTEMS; LINGUISTICS; MEMBERSHIP FUNCTIONS; TREES (MATHEMATICS);

APPROXIMATE REASONINGS; CLASSIFICATION ALGORITHMS; DATA SETS; FEATURE SPACES; FUZZY GRIDS; FUZZY MODELS; LINGUISTIC TERMS; NEURAL FUZZY; NONLINEAR PROCESSES; RULE BASE; SIMULATION RESULTS; UNDERLYING DISTRIBUTIONS; WEIGHTED RULES;

NEURAL NETWORKS;

EID: 56349106592     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2008.4633823     Document Type: Conference Paper

References (39)

1. H. Takagi, "Fusion technology of neural networks and fuzzy systems: A chronicled progression from the laboratory to our daily lives," International Journal of Applied Mathematics and Computer Science, vol. 10, no. 4, pp. 647-673, 2000.
2. L.-X. Wang and J. M. Mendel, "Generating fuzzy rules by learning from examples," IEEE Transactions on Systems, Man and Cybernetics, vol. 22, no. 6, pp. 1414-1427, 1992.
3. J. . Chen, Y. Xi, and Z. Zhang, "A clustering algorithm for fuzzy model, identification," Fuzzy Sets and Systems, vol. 98, no. 3, pp. 319-329, 1998.
4. A. Suárez and J. F. Lutsko, "Globally optimal fuzzy decision trees for classification and regression," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 12, pp. 1297-1311, 1999.
5. S. E. Papadakis and J. B. Theocharis, "A ga-based fuzzy modeling approach for generating tsk models," Fuzzy Sets and Systems, vol. 131, no. 2, pp. 121-152, 2002.
6. C. Olaru and L. Wehenkel, "A complete fuzzy decision tree technique," Fuzzy Sets and Systems, vol. 138, no. 2, pp. 221-254, 2003.
7. L. Magdalena, "A first approach to a taxonomy of fuzzy-neural systems," in IJCAI'95 Workshop on Connectionist-Symbolic Integration: From Unified to Hybrid Approaches, 1995.
8. C. T. Lin and C. S. G. Lee, Neural Fuzzy systems - A Neuro-Fuzzy Synergism to Intelligent Systems. Englewood Cliffs, NJ: Prentice Hall, 1996.
9. C. Quek and W. L. Tung, "A novel approach to the derivation of fuzzy membership functions using the falcon-mart architecture," Pattern Recognition Letters, vol. 22, no. 9, pp. 941-958, 2001.
10. C.-T. Lin and C. S. G. Lee, "Neural-network-based fuzzy logic control and decision system," IEEE Transactions on Computers, vol. 40, no. 12, pp. 1320-1336, 1991.
11. L.-X. Wang and J. M. Mendel, "Back-propagation fuzzy system as nonlinear dynamic system identifiers," 1992, pp. 1409-1418.
12. S.-i. Horikawa, T. Furuhashi, and Y. Uchikawa, "On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm," IEEE Transactions on Neural Networks, vol. 3, no. 5, pp. 801-806, 1992.
13. H. Nomura, I. Hayashi, and N. Wakami, "A learning method of fuzzy inference rules by descent method," 1992, pp. 203-210.
14. H. Ichihashi and M. Tokunaga, "Neuro-fuzzy optimal control of backing up a trailer truck," 1993, pp. 306-315.
15. T. Sudkamp and R. J. Hammell II, "Interpolation, completion, and learning fuzzy rules," IEEE Transactions on Systems, Man and Cy-bernetics, vol. 24, no. 2, pp. 332-342, 1994.
16. C.-J. Lin and C.-T. Lin, "Art-based fuzzy adaptive learning control network," vol. 1, 1994, pp. 1-5.
17. R. R. Yager, "Modeling and formulating fuzzy knowledge bases using neural, networks," Neural Networks, vol. 7, no. 8, pp. 1273-1283, 1994.
18. C.-T. Sun, "Rule-base structure identification in an adaptive-network-based fuzzy inference system," IEEE Transactions on Fuzzy Systems, vol. 2, no. 1, pp. 64-73, 1994.
19. H. Takagi and I. Hayashi, "Nn-driven fuzzy reasoning," International Journal of Approximate Reasoning, vol. 5, no. 3, pp. 191-212, 1991.
20. Y. Lin, G. A. Cunningham III, and S. V. Coggeshall, "Using fuzzy partitions to create fuzzy systems from input-output data and set the initial weights in a fuzzy neural network," IEEE Transactions on Fuzzy Systems, vol. 5, no. 4, pp. 614-621, 1997.
21. E. H. Mamdani, "Application of fuzzy algorithms for control of simple dynamic plant," Proceedings of the Institution of Electrical Engineers, vol. 121, no. 12, pp. 1585-1588, 1974.
22. D. E. Rumelhart, J. L. McClelland, and the PDP Research Group, Parallel Distributed Processing. Cambridge, MA: MIT Press, 1986, vol. 1.
23. J. Moody and C. Darken, in Proceedings of the 1988 Connectionist Models Summer School, G. Hinton, S. T., and D. Touretzsky, Eds., 1988, pp. 133-143.
24. R. A. Jacobs and J. M. I., "A competitive modular connectionis architecture," in Advances in Neural Information Processing System, R. P. Lippmann, M. J. E., and T. D. S., Eds., vol. 3. San Mateo, CA: Morgan Kaufman, 1991, pp. 767-773.
25. D. Nauck and R. Kruse, "How the learning of rule weights affects the interpretability of fuzzy systems," vol. 2, 1998, pp. 1235-1240.
26. P. M. Larsen, "Industrial applications of fuzzy logic control," International Journal of Man-Machine Studies, vol. 12, no. 1, pp. 3-10, 1980.
27. J. S. R. Jang, C T. Sun, and E. Mizutano, Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Upper Saddle River, NJ: Prentice Hall, 1997.
28. J. Kim and N. Kasabov, "Hyfis: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems," Neural Networks, vol. 12, no. 9, pp. 1301-1319, 1999.
29. L. I. Kuncheva, "How good are fuzzy if-then classifiers?" IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 30, no. 4, pp. 501-509, 2000.
30. H. Ishibuchi and T. Nakashima, "Effect of rule weights in fuzzy rule-based classification systems," IEEE Transactions on Fuzzy Systems, vol. 9, no. 4, pp. 506-515, 2001.
31. M. Sugeno and T. Yasukawa, "Fuzzy-logic-based approach to qualitative modeling," IEEE Transactions on Fuzzy Systems, vol. 1, no. 1, pp. 7-31, 1993.
32. D. E. Rumelhart, G. E. Hinton, and W. R. J., "Learning internal representations by error propagation," in Parallel Distributed Processing, D. E. Rumelhart and M. J. L., Eds. Cambridge, MA: MIT Press, 1986, vol. 1, ch. 8.
33. G. E. P. Box and G. M. Jenkins, Time Series Analysis- Forecasting and Control. San Francisco: Holden Day: Prentice Hall, 1970.
34. S. . Oh and W. Pedrycz, "The design of self-organizing polynomial neural, networks," Information Sciences, vol. 141, no. 3-4, pp. 237-258, 2002.
35. D. Kukolj and E. Levi, "Identification of complex systems based on neural and takagi-sugeno fuzzy model," IEEE Transactions on Systems, Man and Cybernetics, Part B volume=34, number=1, pages=272-282, 2004.
36. J. Nie, "Constructing fuzzy model by self-organizing counterpropagation network," IEEE Transactions on Systems, Man and Cybernetics, vol. 25, no. 6, pp. 963-970, 1995.
37. W Pedrycz, P. Lam, and A. Rocha, "Distributed fuzzy system modeling," IEEE Transactions on Systems, Man and Cybernetics, vol. 25, no. 5, pp. 769-780, 1995.
38. S. Oh, W. Pedrycz, and H. Park, "Rule-based multi-fun identification with the aid of evolutionary fuzzy granulation," Knowledge-Based Systems, vol. 17, no. 1, pp. 1-13, 2004.
39. S. Purwar, I. N. Kar, and A. N. Jha, "On-line system identification of complex systems using chebyshev neural networks," Applied Soft Computing Journal, vol. 7, no. 1, pp. 364-372, 2007.