SaFIN: A self-adaptive fuzzy inference network

IEEE Transactions on Neural Networks

Volumn 22, Issue 12 PART 1, 2011, Pages 1928-1940

  Tung, Sau Wai ^a   Quek, Chai ^a   Guan, Cuntai ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

Categorical learning induced partitioning; fuzzy neural networks; hybrid intelligent systems; self organizing

Indexed keywords

CATEGORICAL LEARNING-INDUCED PARTITIONING; CLUSTERING TECHNIQUES; EXCELLENT PERFORMANCE; FUZZY-NEURAL; HUMAN EXPERT; HYBRID INTELLIGENT SYSTEM; INFERENCE NETWORK; INPUT-OUTPUT; ITS EFFICIENCIES; LEARNING PROCESS; NEURAL FUZZY SYSTEMS; NUMBER OF CLUSTERS; ONE-PASS; PRIOR KNOWLEDGE; RULE BASE; RULE FORMATION; SELF ORGANIZING; SELF-ADAPTIVE; TRAINING DATA;

COMPUTER SIMULATION; DESIGN; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; INTELLIGENT SYSTEMS; KNOWLEDGE ACQUISITION;

FUZZY INFERENCE;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; EXPERT SYSTEM; FEEDBACK SYSTEM; FUZZY LOGIC; HUMAN; THEORETICAL MODEL;

ALGORITHMS; COMPUTER SIMULATION; EXPERT SYSTEMS; FEEDBACK; FUZZY LOGIC; HUMANS; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER);

EID: 83855161643     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2011.2167720     Document Type: Article

References (44)

1. D. Nauck, F. Klawonn, and R. Kruse, Foundations of Neuro-Fuzzy Systems. New York: Wiley, 1997.
2. S. Guillaume, "Designing fuzzy inference systems from data: An interpretability-oriented review," IEEE Trans. Fuzzy Syst., vol. 9, no. 3, pp. 426-442, Jun. 2001.
3. J. S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference systems," IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665-685, May-Jun. 1993.
4. H. R. Berenji and P. Khedkar, "Learning and tuning fuzzy logic controllers through reinforcements," IEEE Trans. Neural Netw., vol. 3, no. 5, pp. 724-740, Sep. 1992. (Pubitemid 23555780)
5. J. Casillas, O. Cordon, F. Herrera, and L. Magdalena, Interpretability in Fuzzy Modeling. New York: Springer-Verlag, 2003, pp. 605-620.
6. C. T. Lin and C. S. G. Lee, "Neural-network-based fuzzy logic control and decision system," IEEE Trans. Compt., vol. 40, no. 12, pp. 1320-1336, Dec. 1991.
7. C. Quek and R. W. Zhou, "POPFNN: A pseudo outer-product based fuzzy neural network," Neural Netw., vol. 9, no. 9, pp. 1569-1581, Dec. 1996. (Pubitemid 27055275)
8. C. J. Lin and C. T. Lin, "An ART-based fuzzy adaptive learning control network," IEEE Trans. Fuzzy Syst., vol. 5, no. 4, pp. 477-496, Nov. 1997.
9. C. F. Juang and C. T. Lin, "An online self-constructing neural fuzzy inference network and its applications," IEEE Trans. Fuzzy Syst., vol. 6, no. 1, pp. 12-32, Feb. 1998.
10. J. Kim and N. Kasabov, "HyFIS: Adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems," Neural Netw., vol. 12, no. 9, pp. 1301-1319, Nov. 1999. (Pubitemid 29475159)
11. N. Kasabov, "Evolving fuzzy neural networks for super-vised/ unsupervised online knowledge-based learning," IEEE Trans. Syst. Man Cybern. B, vol. 31, no. 6, pp. 902-918, Dec. 2001. (Pubitemid 34035554)
12. W. L. Tung and C. Quek, "GenSoFNN: A generic self-organizing fuzzy neural network," IEEE Trans. Neural Netw., vol. 13, no. 5, pp. 1075-1086, Sep. 2002.
13. N. Kasabov and Q. Song, "DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction," IEEE Trans. Fuzzy Syst., vol. 10, no. 2, pp. 144-154, Apr. 2002. (Pubitemid 34554860)
14. P. P. Angelov and D. P. Filev, "An approach to online identification of Takagi-Sugeno fuzzy models," IEEE Trans. Syst. Man Cybern., vol. 34, no. 1, pp. 484-498, Feb. 2004.
15. H. J. Rong, N. Sundararajan, G. B. Huang, and P. Saratchandra, "Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction," Fuzzy Sets Syst., vol. 157, no. 9, pp. 1260-1275, May 2006.
16. W. L. Tung and C. Quek, "eFSM-A novel online neural-fuzzy semantic memory model," IEEE Trans. Neural Netw., vol. 21, no. 1, pp. 136-157, Jan. 2010.
17. J. Tan and C. Quek, "A BCM theory of meta-plasticity for online self-reorganizing fuzzy-associative leaning," IEEE Trans. Neural Netw., vol. 21, no. 6, pp. 985-1003, Jun. 2010.
18. J. C. Bezdek, E. C.-K. Tsao, and N. R. Pal, "Fuzzy Kohonen clustering networks," in Proc. IEEE Conf. Fuzzy Syst., San Diego, CA, Mar. 1992, pp. 1035-1043.
19. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1981.
20. T. Kohonen, "Self-organized formation of topologically correct feature maps," Biol. Cybern., vol. 43, no. 1, pp. 59-69, 1982. (Pubitemid 12139984)
21. L. X. Wang and J. M. Mendel, "Generating fuzzy rules by learning from examples," IEEE Trans. Syst. Man Cybern., vol. 22, no. 1, pp. 1414-1427, Nov.-Dec. 1992.
22. K.-L. Du, "Clustering: A neural network approach," Neural Netw., vol. 23, no. 1, pp. 89-107, Jan. 2010.
23. S. Grossberg, Studies of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition, and Motor Control. Boston, MA: Reidel, 1982.
24. J. M. Mandler and P. J. Bauer, "The cradle of categorization: Is the basic level basic?," Cognitive Develop., vol. 3, no. 3, pp. 247-264, 1988.
25. J. M. Mandler, P. J. Bauer, and L. McDonough, "Separating the sheep from the goats: Differentiating global categories," Cognitive Psychology, vol. 23, no. 2, pp. 263-298, Apr. 1991.
26. J. M. Mandler and L. McDonough, "Concept formation in infancy," Cognitive Develop., vol. 8, no. 3, pp. 291-318, Jul.-Sep. 1993.
27. J. M. Mandler and L. McDonough, "On developing a knowledge base in infancy," Develop. Psychology, vol. 34, no. 6, pp. 1274-1288, 1998.
28. J. M. Mandler and L. McDonough, "Studies in inductive inference in infancy," Cognitive Psychology, vol. 37, no. 1, pp. 60-96, Oct. 1998. (Pubitemid 128428614)
29. J. M. Mandler, "On the birth and growth of concepts," Philosophical Psychology, vol. 21, no. 2, pp. 207-230, Apr. 2008.
30. E. H. Mamdani, "Application of fuzzy logic to approximate reasoning using linguistic systems," IEEE Trans. Comp., vol. 26, no. 12, pp. 1182-1191, Dec. 1977.
31. S. Haykin, Neural Network: A Comprehensive Foundation. Englewood Cliffs, NJ: Prentice Hall, 1998.
32. C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Englewood Cliffs, NJ: Prentice Hall, 1996.
33. S. W. Tung, C. Quek, and C. Guan, "eT2FIS: An evolving type-2 neural fuzzy inference system," Inform. Sci., submitted.
34. H. Nakanishi, I. Turksen, and M. Sugeno, "A review and comparison of six reasoning methods," Fuzzy Sets Syst., vol. 57, no. 3, pp. 257-294, Aug. 1993.
35. M. Sugeno and T. Yasukawa, "A fuzzy-logic-based approach to qualitative modeling," IEEE Trans. Fuzzy Syst., vol. 1, no. 1, pp. 7-31, Feb. 1993.
36. G. K. Tan, "Feasibility of predicting congestion states with neural network models," M.S. thesis, School Civil Structural Engineering, Nanyang Technological Univ., Singapore, 1997.
37. UCI Machine Learning Repository [Online]. Available: http://archive.ics. uci.edu/ml
38. F. Liu, C. Quek, and G. S. Ng, "A novel generic hebbian ordering-based fuzzy rule base reduction approach to Mamdani neuro-fuzzy system," Neural Comput., vol. 19, no. 6, pp. 1656-1680, Apr. 2007.
39. K. K. Ang and C. Quek, "RSPOP: Rough set-based pseudo outer-product fuzzy rule identification algorithm," Neural Comput., vol. 17, no. 1, pp. 205-243, Jan. 2005. (Pubitemid 40305888)
40. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Trans. Syst. Man Cybern., vol. 15, no. 1, pp. 116-132, Feb. 1985.
41. K. H. Quah and C. Quek, "FITSK: Online local learning with generic fuzzy input Takagi-Sugeno-Kang fuzzy framework for nonlinear system estimation," IEEE Trans. Syst. Man Cybern. B, vol. 36, no. 1, pp. 166-178, Feb. 2006. (Pubitemid 43174150)
42. T. M. Cover and P. E. Hart, "Nearest pattern classification," IEEE Trans. Inform. Theory, vol. 13, no. 1, pp. 21-27, Jan. 1967.
43. P. K. Simpson, "Fuzzy min-max neural networks-Part I: Classification," IEEE Trans. Neural Netw., vol. 3, no. 5, pp. 776-786, Sep. 1992.
44. B. Gabrys and A. Bargiela, "General fuzzy min-max neural network for clustering and classification," IEEE Trans. Neural Netw., vol. 11, no. 3, pp. 769-783, May 2000.