Data driven modeling based on dynamic parsimonious fuzzy neural network

Neurocomputing

Volumn 110, Issue , 2013, Pages 18-28

  Pratama, Mahardhika ^a   Er, Meng Joo ^b   Li, Xiang ^c   Oentaryo, Richard J ^b   Lughofer, Edwin ^d   Arifin, Imam ^e  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c SINGAPORE INSTITUTE OF MANUFACTURING TECHNOLOGY   (Singapore)

^d JOHANNES KEPLER UNIVERSITY LINZ   (Austria)

^e DEPARTMENT OF ELECTRICAL ENGINEERING   (Indonesia)

Author keywords

Dynamic parsimonious fuzzy neural network (DPFNN); Radial basis function (RBF); Rule growing; Rule pruning; Self organizing map (SOM)

Indexed keywords

COMPUTATIONAL BURDEN; ELLIPSOIDAL BASIS FUNCTIONS; LEAST SQUARES METHODS; NONSTATIONARY PROCESS; RADIAL BASIS FUNCTION(RBF); RULE PRUNING; SELF-ORGANIZING MAP (SOM); STATE-OF-THE-ART APPROACH;

COALESCENCE; COMPLEX NETWORKS; FUZZY RULES; LEAST SQUARES APPROXIMATIONS; NETWORK LAYERS; RADIAL BASIS FUNCTION NETWORKS; SELF ORGANIZING MAPS;

FUZZY NEURAL NETWORKS;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; DYNAMIC PARSIMONIOUS FUZZY NEURAL NETWORK; EXTENDED SELF ORGANIZING MAP THEORY; FUZZY SYSTEM; KERNEL METHOD; LEARNING ALGORITHM; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; METHODOLOGY; PRIORITY JOURNAL; REGRESSION ANALYSIS; RELIABILITY; THEORY; VALIDATION PROCESS;

EID: 84876111017     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.11.013     Document Type: Article

References (68)

1. Wang L.X., Mendel J.M. Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans. Neural Networks 1992, 3:807-814.
2. Platt J. A resource allocating network for function interpolation. Neural Comput. 1991, 3:213-225.
3. Salmerón M., Ortega J., Puntonet C.G., Prieto A. Improved RAN sequential prediction using orthogonal techniques. Neurocomputing 2001, 41:153-172.
4. Yingwei L., Sundararajan N., Saratchandran P. Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Trans. Neural Networks 1998, 9:308-318.
5. Saman H.K. Self evolving neural network for rule base data processing. IEEE Trans. Signal Process. 1997, 45:2766-2773.
6. Huang G.-B., Saratchandran P., Sundararajan N. An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2004, 34:2284-2292.
7. Huang G.-B., Saratchandran P., Sundararajan N. A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation. IEEE Trans. Neural Networks 2005, 16:57-67.
8. Zadeh L.A. Soft computing and fuzzy logic. IEEE Softw. 1994, 11:48-56.
9. Jang J.-S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. Part B Cybern. 1999, 23:665-684.
10. Wu S., Er M.J. Dynamic fuzzy neural networks-a novel approach to function approximation. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2000, 30:358-364.
11. Er M.J., Fast S. Learning algorithm for parsimonious fuzzy neural network. Fuzzy Sets Syst. 2002, 126:337-351.
12. Chen S., Cowan C.F.N., Grant P.M. Orthogonal least squares learning algorithm for radial basis function network. IEEE Trans. Neural Networks 1991, 2:302-309.
13. Wu S.-Q., Er M.J., Gao Y. A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans. Fuzzy Syst. 2001, 9:578-594.
14. Gao Y., Er M.J. NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches. Fuzzy Sets Syst. 2005, 150:331-350.
15. Juang C.F., Lin C.T. An on-line self-constructing neural fuzzy inference network and its applications. IEEE Trans. Fuzzy Syst. 1998, 6:12-32.
16. Kasabov N., Song Q. DENFIS: dynamic evolving neural-fuzzy inference system and its application for time series prediction. IEEE Trans. Fuzzy Syst. 2002, 10:144-154.
17. Leng G., McGinnity T.M., Prasad G. An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network. Fuzzy Sets Syst. 2005, 150:211-243.
18. Leng G., Prasad G., McGinnity T.M. An on-line algorithm for creating self-organizing fuzzy neural networks. Neural Networks 2004, 170:1477-1493.
19. Leng G., McGinnity T.M., Prasad G. Design for self organizing fuzzy neural network based on genetic algorithm. IEEE Trans. Fuzzy Syst. 2006, 14:755-766.
20. Wang N., Er M.J., Fast M.X. Accurate self organizing scheme for parsimonious fuzzy neural network. Neurocomputing 2009, 72:3818-3829.
21. Rong H.J., Sundararajan N., Huang G.B., Saratchandran P. Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and time series prediction. Fuzzy Sets Syst. 2006, 157:1260-1275.
22. Zhou.Y., Er M.J. A novel approach for generation of fuzzy neural networks. Int. J. Fuzzy Syst. 2007, 7:8-13.
23. Er M.J., Zhou Y. Automatic generation of fuzzy inference systems via unsupervised learning. Neural Networks 2008, 21:1556-1566.
24. Er M.J., Wu S., Gao Y. Dynamic Fuzzy Neural Networks: Architectures, Algorithms and Applications 2003, McGraw-Hill, NY, USA.
25. L. Wang, Fuzzy systems are universal approximators, in: Proc. International Conference on Fuzzy Systems, (1992), pp. 1163-1169.
26. Kohonen T. Self-organized formation of topologically correct feature maps. Biol. Cybern. 1982, 43(1982):59-69.
27. Er M.J., et al. Adaptive noise cancellation using enhanced dynamic fuzzy neural networks. IEEE Trans. Fuzzy Syst. 2005, 13:331-342.
28. Tung W.L., Quek C. eFSM-A novel online neural-fuzzy semantic memory model. IEEE Trans. Neural Networks 2010, 21:136-157.
29. S. Huang, X. Li, O.P. Gan, Tool wear estimation using SVM in ball nose end milling, IEEE Annual Conference of The Prognostic and Health Society, 2010.
30. Sugeno M., Kang G.T. Structure identification of fuzzy model. Fuzzy Sets Syst. 1988, 28:15-33.
31. Leung C.S., Wong K.W., Sum P.F., Chan L.W. A pruning method for the recursive least squared algorithm. Neural Networks 2001, 14:147-174.
32. Zhou J.H., Pang C.K., Lewis F.L., Zhong Z.W. Intelligent diagnosis and prognosis of tool wear using dominant feature identification. IEEE Trans. Ind. Inf. 2009, 5:454-464.
33. Rehorn A.G., Jiang J., Orban P.E. State-of-the-art methods and results in tool condition monitoring: a review. Int. J. Adv. Manuf. Technol. 2005, 26:693-710.
34. Lee C.C. Fuzzy logic in control systems: fuzzy logic controller. IEEE Trans. Syst. Man Cybern. Part B Cybern. 1990, 20:404-436.
35. Mackey M.C., Glass L. Oscillation and chaos in physiological control systems. Science 1977, 197:287-289.
36. Angelov P., Filev D. An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2004, 34:484-498.
37. P. Angelov, D. Filev, Simpl_eTS: A simplified method for learning evolving Takagi-Sugeno fuzzy models, in: IEEE International Conference on Fuzzy Systems (FUZZ), 2005, pp. 1068-1073.
38. Lughofer E. FLEXFIS: a robust incremental learning approach for evolving Takagi-Sugeno fuzzy models,. IEEE Trans. Fuzzy Syst. 2008, 16:1393-1410.
39. Ning W., Er M.J., Xian-Yao M., Li X. An online self organizing scheme for parsimonious and accurate fuzzy neural networks. Int. J. Neural Syst. 2010, 10:389-403.
40. Stone M. Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. 1974, 36:111-147.
41. P. Angelov, Evolving rule-based models: a tool for design flexible adaptive systems, in: The Series Studies in Fuzziness and Soft Computing. Heidelberg Germany: Springer, Physics-Verlag, 2002, vol. 92.
42. Jang J.S.R., Sun C.T. Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Trans. Neural Networks 1993, 4:156-159.
43. Pedrycs W. An identification algorithm in fuzzy relational system. Fuzzy Sets Syst. 1984, 13:153-167.
44. Takagi T., Sugeno M. Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man Cybern. Part B Cybern. 1985, 15:116-132.
45. Angelov P., Buswell R. Identification of evolving fuzzy rule based models. IEEE Trans. Fuzzy Syst. 2002, 16:667-676.
46. Li X., Er M.J., Lim B.S., Zhou J.H., Gan O.P., Rutkowski L. Fuzzy regression modeling for tool performance prediction and degradation detection. Int. J. Neural Syst. 2010, 20:405-419.
47. Lee B.Y., Liu H.S., Tarng Y.S. Modeling and optimization of drilling process. J. Mater. Process. Technol. 1998, 74:149-157.
48. Perusich K. Using fuzzy cognitive maps to identify multiple causes in troubleshooting systems. Integr. Comput. Aided Eng. 2008, 15:197-206.
49. Zurada J.M. Introduction to Artificial Neural System 1992, West Publishing Company, USA.
50. Haddadi E., Shabghard M.R., Ettefagh M.M. Effect of different tool edge conditions on wear detection by vibration spectrum analysis in turning operation. J. Appl. Sci. 2008, 8:3879-3886.
51. Wang L., Mehrabi M.G., Jr E.K. Tool wear monitoring in reconfigurable machining systems through wavelet analysis. Trans. NAMRI 2001, 3:399-406.
52. Lughofer E. Evolving Fuzzy Systems-Methodologies, Advanced Concepts and Applications 2011, Springer, Berlin Heidelberg.
53. Klement E.P., Mesiar R., Pap E. Triangular Norms 2000, Kluwer Academic Publishers, Dordrecht Norwell New York London.
54. Dickerson J.A., Kosko B. Fuzzy function approximations with ellipsoidal rules. IEEE Trans. Syst. Man Cybern. Part B Cybern. 1996, 26:542-560.
55. Abe S. Fuzzy function approximators with ellipsoidal regions. IEEE Trans. Syst. Man Cybern. Part B Cybern. 1999, 29:654-661.
56. Lemos A., Caminhas W., Gomide F. Multivariable Gaussian evolving fuzzy modeling system. IEEE Trans. Fuzzy Syst. 2011, 19:91-104.
57. Leung C.S., Wong K.W., Sum P.F., Chan L.W A pruning method for the recursive least squared algorithm. Neural Networks 2001, 14:147-174.
58. Rubio J.D.J. SOFMLS: online self-organizing fuzzy modified least squares network. IEEE Trans. Fuzzy Syst. 2009, 17:1296-1309.
59. Werbos P.J. The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting 1994, Wiley, Hoboken, NJ.
60. Abonyi J., Babuska R., Szeifert F. Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models. IEEE Trans. Fuzzy Syst. 2002, 33:612-621.
61. Lughofer E. Extension of vector quantization for incremental clustering. Pattern Recognit. 2008, 41:995-1011.
62. Chun-Ru Dong Xi-Zhao Wang, Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy. IEEE Trans. Fuzzy Syst. 2009, 17:556-567.
63. Wang Xi-Zhao, Zhai Jun-Hai, Lu Shu-Xia Induction of multiple fuzzy decision trees based on rough set technique. Inf. Sci. 2008, 178:3188-3202.
64. Wang Xi-Zhao, Dong Chun-Ru Tie-Gang fan training T-S norm neural networks to refine weights for fuzzy if-then rules. Neurocomputing 2007, 70:2581-2587.
65. Lemos A., Caminhas W., Gomide F. Multivariable Gaussian evolving fuzzy modelling system. IEEE Trans. Fuzzy Syst. 2011, 19:91-104.
66. R.J. Oentaryo, M.J. Er, L. San, L.-Y. Zhai, X. Li, Bayesian ART-based fuzzy inference system: a new approach to prognosis of machining process, in: IEEE Annual Conference of The Prognostic and Health Society, 2011.
67. Lughofer E. Flexible evolving fuzzy inference systems from data streams (FLEXFIS++). Learning in Non-Stationary Environments: Methods and Applications 2012, 205-246. Springer, New York. M. Sayed-Mouchaweh, E. Lughofer (Eds.).
68. Haykin S. Neural Networks: A Comprehensive Foundation 1999, Prentice Hall Inc., Upper Saddle River, New Jersey. second ed.