PANFIS: A novel incremental learning machine

IEEE Transactions on Neural Networks and Learning Systems

Volumn 25, Issue 1, 2014, Pages 55-68

  Pratama, Mahardhika ^a   Anavatti, Sreenatha G ^a   Angelov, Plamen P ^b   Lughofer, Edwin ^c  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

^b LANCASTER UNIVERSITY   (United Kingdom)

^c JOHANNES KEPLER UNIVERSITY LINZ   (Austria)

Author keywords

Evolving neuro fuzzy systems (ENFSs); incremental learning; sample wise training

Indexed keywords

COMPUTATIONAL LOADS; DEGREE OF FLEXIBILITY; DEGREE OF NON-LINEARITY; FUZZY INFERENCE SYSTEMS; GROWING AND PRUNING; INCREMENTAL LEARNING; NEUROFUZZY SYSTEM; NON-STATIONARY ENVIRONMENT;

ALGORITHMS; COMPLEX NETWORKS; FUZZY RULES; FUZZY SYSTEMS; LEARNING SYSTEMS;

KNOWLEDGE ENGINEERING;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; FEEDBACK SYSTEM; PROCEDURES; STATISTICAL MODEL;

ALGORITHMS; COMPUTER SIMULATION; FEEDBACK; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED;

EID: 84891162354     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2013.2271933     Document Type: Article

References (57)

1. L. A. Zadeh, "Soft computing and fuzzy logic," IEEE Softw., vol. 11, no. 6, pp. 48-56, Nov. 1994.
2. L. T. Whye and Q. Chai, "eFSM-A novel online neural fuzzy semantic memory model," IEEE Trans. Neural Netw., vol. 21, no. 1, pp. 136-157, Jan. 2010.
3. E. Lughofer, Evolving Fuzzy Systems-Methodologies, Advanced Concepts and Applications. New York, NY, USA: Springer-Verlag, 2011
4. W. A. Farag, V. H. Quintana, and G. Lambert-Torres, "A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems," IEEE Trans. Neural Netw., vol. 9, no. 5, pp. 756-767, Sep. 1998. (Pubitemid 128743644)
5. E. Lughofer and P. Angelov, "Handling drifts and shifts in on-line data streams with evolving fuzzy systems," Appl. Soft Comput. (Elsevier), vol. 11, no. 2, pp. 2057-2068, 2011.
6. S. Wu and M. J. Er, "Dynamic fuzzy neural networks-A novel approach to function approximation," IEEE Trans. Syst. Man Cybern., Part B, Cybern., vol. 30, no. 2, pp. 358-364, Apr. 2000.
7. G. Leng, G. Prasad, and T. M. McGinnity, "An on-line algorithm for creating self-organizing fuzzy neural networks," Neural Netw., vol. 17, no. 10, pp. 1477-1493, 2004. (Pubitemid 39487143)
8. R. M. French, "Catastrophic forgetting in connectionist networks," in Encyclopedia of Cognitive Science, vol. 1. L. Nadel, Ed. London, U.K.: Nature Publishing Group, 2003, pp. 431-435.
9. E. Lughofer and P. Angelov, "Handling drifts and shifts in on-line data streams with evolving fuzzy systems," Appl. Soft Comput. (Elsevier), vol. 11, no. 2, pp. 2057-2068, 2011.
10. G.-B. Huang, P. Saratchandran, and N. Sundararajan, "A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation," IEEE Trans. Neural Netw., vol. 16, no. 1, pp. 57-67, Feb. 2005. (Pubitemid 40241910)
11. P. Angelov and D. Filev, "An approach to online identification of Takagi-Sugeno fuzzy models," IEEE Trans. Syst., Man, Cybern., Part B, Cybern., vol. 34, no. 1, pp. 484-498, Feb. 2004.
12. E. Lughofer, "FLEXFIS: A robust incremental learning approach for evolving Takagi-Sugeno fuzzy models," IEEE Trans. Fuzzy Syst., vol. 16, no. 6, pp. 1393-1410, Dec. 2008.
13. H. J. Rong, N. Sundararajan, G. B. Huang, and P. Saratchandran, "Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and time series prediction," Fuzzy Sets Syst., vol. 157, no. 9, pp. 1260-1275, 2006.
14. J. A. Dickerson and B. Kosko, "Fuzzy function approximations with ellipsoidal rules," IEEE Trans. Syst., Man, Cybern. Part B, Cybern., vol. 26, no. 4, pp. 542-560, Aug. 1996. (Pubitemid 126780595)
15. A. Lemos, W. Caminhas, and F. Gomide, "Multivariable Gaussian evolving fuzzy modeling system," IEEE Trans. Fuzzy Syst., vol. 19, no. 1, pp. 91-104, Feb. 2011.
16. P. Angelov, "Evolving Takagi-Sugeno fuzzy systems from data streams (eTS+)," in Evolving Intelligent Systems: Methodology and Applications, P. Angelov, D. Filev, and N. Kasabov Eds., New York, NY, USA: Willey, Apr. 2010, pp. 21-50.
17. M. Setnes, "Simplification and reduction of fuzzy rules," Interpretability Issues In Fuzzy Modeling. Studies in Fuzziness and Soft Computing, J. Casillas, O. Cordón, F. Herrera, and L. Magdalena, Eds. vol. 128. New York, NY, USA: Springer-Verlag, 2003, pp. 278-302.
18. E. Lughofer, J.-L. Bouchot, and A. Shaker, "On-line elimination of local redundancies in evolving fuzzy systems," Evolving Syst., vol. 2, no. 3, pp. 380-387, 2011.
19. E. H. Mamdani and S. Assilian, "An experiment in linguistic synthesis with fuzzy logic controller," Int. J. Man-Mach. Stud., vol. 7, no. 1, pp. 1-13, 1975.
20. W. Pedrycs, "An identification algorithm in fuzzy relational system," Fuzzy Sets Syst., vol. 13, no. 2, pp. 153-167, 1984.
21. P. Angelov and R. Yager, "A new type of simplified fuzzy rule-based systems," Int. J. General Syst., vol. 41, no. 2, pp. 163-185, 2011.
22. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its appfications to modeling and control," IEEE Trans. Syst. Man Cybern., vol. 15, no. 1, pp. 116-132, Jan./Feb. 1985.
23. J. L. Castro and M. Delgado, "Fuzzy systems with defuzzification are universal approximators," IEEE Trans. Syst., Man Cybern., Part B, Cybern., vol. 26, no. 1, pp. 149-152, Feb. 1996. (Pubitemid 126780584)
24. L. Wang, "Fuzzy systems are universal approximators," in Proc. IEEE Int. Conf. Fuzzy Syst., Mar. 1992, pp. 1163-1170. (Pubitemid 23590929)
25. C. C. Lee, "Fuzzy logic in control systems: Fuzzy logic controller. II," IEEE Trans. Syst., Man, Cybern., vol. 20, no. 2, pp. 404-436, Mar./Apr. 1990.
26. T. Kohonen, "Self-organized formation of topologically correct feature maps," Biol. Cybern., vol. 43, no. 1, pp. 59-69, 1982. (Pubitemid 12139984)
27. B. Vigdor and B. Lerner, "The Bayesian ARTMAP," IEEE Trans. Neural Netw., vol. 18, no. 6, pp. 1628-1644, Nov. 2007. (Pubitemid 350148417)
28. S. Grossberg, Studies of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition, and Motor Control. Boston, MA, USA: Reidel, 1982.
29. P. Angelov and D. Filev, "A simplified method for learning evolving Takagi-Sugeno fuzzy models," in Proc. IEEE Int. Conf. Fuzzy Syst. FUZZ, May 2005, pp. 1068-1073. (Pubitemid 41189654)
30. S. Chiu, "Fuzzy model identification based on cluster estimation," J. Intell. Fuzzy Syst., vol. 2, no. 3, pp. 267-278, 1994.
31. C. R. Rao and S. K. Mitra, Generalized Inverse of Matrices and its Applications. New York, NY, USA: Wiley, 1971.
32. G. Huang, Q. Zhu, and C. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, vol. 70, pp. 489-501, Dec. 2006. (Pubitemid 44615772)
33. K. J. Astrom and B. Wittenmark, Adaptive Control, 2nd ed. Reading, MA, USA: Addison Wesley, 1995.
34. 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)
35. N. K. Kasabov, "Evolving fuzzy neural networks for supervised/ unsupervised online knowledge-based learning," IEEE Trans. Syst., Man, Cybern., Part B, Cybern., vol. 31, no. 6, pp. 902-918, Dec. 2001.
36. T. Javan and Q. Chai, "A BCM theory of meta plasticity for online self reorganizing fuzzy-Associative learning," IEEE Trans. Neural Netw., vol. 21, no. 6, pp. 985-1003, Jun. 2010.
37. N. Wang, M. J. Er, and X. Meng, "A fast and accurate self organizing scheme for parsimonious fuzzy neural network, Neurocomputing, vol. 72, nos. 16-18, pp. 3818-3829, 2009.
38. R. J. Oentaryo, M. J. Er, L. San, L.-Y. Zhai, and X. Li, "Bayesian ART-based fuzzy inference system: A new approach to prognosis of machining process," in Proc. IEEE Annu. Conf. Prognostic Health Soc., Jun. 2011, pp. 1-10.
39. H. Han and J. Qiao, "A self-organizing fuzzy neural network based on a growing-And-pruning algorithm," IEEE Trans. Fuzzy Syst., vol. 18, no. 6, pp. 1129-1143, Dec. 2010.
40. J. D. J. Rubio, "SOFMLS: Online self-organizing fuzzy modified least squares network," IEEE Trans. Fuzzy Syst., vol. 17, no. 6, pp. 1296-1309, Dec. 2009.
41. E. Lughofer, V. Macian, C. Guardiola, and E. P. Klement, "Identifying static and dynamic prediction models for NOx emission with evolving fuzzy system," Appl. Soft Comput., vol. 11, no. 2, pp. 2487-2500, 2011.
42. C.-T. Lin, and C. S. G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Upper Saddle River, NJ, USA: Prentice- Hall, 1996.
43. N. K. 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)
44. A. Bifet, G. Holmes, R. Kirkby, and B. P. Fahringer, "MOA: Massive online analysis," J. Mach. Learn. Res., vol. 11, pp. 1601-1604, May 2010.
45. C. F. Juang and C. T. Lin, "An on-line self-constructing neural fuzzy inference network and its applications," IEEE Trans. Fuzzy Syst., vol. 6, no. 1, pp. 12-32, Feb. 1998.
46. K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Netw., vol. 2, no. 5, pp. 359-366, 1989. (Pubitemid 20609008)
47. J.-S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Trans. Syst. Man. Cybern. Part B, Cybern., vol. 23, no. 3, pp. 665-684, May/Jun. 1993.
48. M. J. Watts, "A decade of Kasabov's evolving connectionist systems: A review," IEEE Trans. Syst. Man Cybern. C, Appl. Rev., vol. 39, no. 3, pp. 253-269, May 2009.
49. R. Polikar, L. Udpa, S. S. Udpa, and V. Honavar, "Learn++: An incremental learning algorithm for supervised neural networks," IEEE Trans. Syst., Man Cybern. Part C, Appl., vol. 31, no. 4, pp. 497-508, Nov. 2001. (Pubitemid 34191829)
50. M. D. Muchbailer, A. Topalis, and R. Polikar, "Learn++.NC: Combining ensemble of classifiers with dynamically weighted consult-And-vote for efficient incremental learning of new classes," IEEE Trans. Neural Netw., vol. 20, no. 1, pp. 152-168, Jan. 2009.
51. R. Elwell and R. Polikar, "Incremental learning of concept drift in nonstationary environments," IEEE Trans. Neural Netw., vol. 22, no. 11, pp. 1517-1531, Oct. 2011.
52. C. F. Juang and Y. W. Tsao, "A self-evolving interval type-2 fuzzy neural network with on-line structure and parameter learning," IEEE Trans. Fuzzy Syst., vol. 16, no. 6, pp. 1411-1424, Dec. 2008.
53. C. F. Juang, R. B. Huang, and Y. Y. Lin, "A recurrent self-evolving interval type-2 fuzzy neural network for dynamic system processing," IEEE Trans. Fuzzy Syst., vol. 17, no. 5, pp. 1092-1105, Oct. 2009.
54. C. F. Juang, T. C. Chen, and W. Y. Cheng, "Speedup of implementing fuzzy neural networks with high-dimensional inputs through parallel processing on graphic processing units," IEEE Trans. Fuzzy Syst., vol. 19, no. 4, pp. 717-728, Aug. 2011.
55. G. D. Wu, Z. W. Zhu, and P. H. Huang, "A TS-type maximizingdiscriminability- based recurrent fuzzy network for classification problems," IEEE Trans. Fuzzy Syst., vol. 19, no. 2, pp. 339-352, Apr. 2011.
56. W. N. Street and Y. Kim, "A streaming ensemble algorithm (SEA) for large-scale classification," in Proc. Int. Conf. Knowl. Discovery Data Mining, 2001, pp. 377-382.
57. J. Z. Kolter and M. A. Maloof, "Dynamic weighted majority: An ensemble method for drifting concepts," J. Mach. Learn. Res., vol. 8, pp. 2755-2790, Dec. 2007.