A new gradient descent approach for local learning of fuzzy neural models

IEEE Transactions on Fuzzy Systems

Volumn 21, Issue 1, 2013, Pages 30-44

  Zhao, Wanqing ^a   Li, Kang ^a   Irwin, George W ^a  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

Fuzzy systems; gradient descent; interpretability; local learning; Takagi Sugeno Kang (TSK) model

Indexed keywords

BLACK-BOX MODEL; CRITICAL STEPS; DESIGN REQUIREMENTS; FUZZY MODELS; FUZZY NEURAL MODELS; FUZZY PARTITION; FUZZY-NEURAL; GRADIENT DESCENT; IN-BUILDINGS; INTERPRETABILITY; LEARNING APPROACH; LEARNING METHODS; LEVENBERG-MARQUARDT OPTIMIZATION; LINEAR PARAMETERS; LOCAL LEARNING; NON-LINEAR PARAMETERS; NUMERICAL EXAMPLE; SECOND ORDERS; SYSTEM BEHAVIORS; SYSTEM OUTPUT; TAKAGI-SUGENO-KANG MODEL; TWO STAGE OPTIMIZATIONS;

FUZZY SYSTEMS; JACOBIAN MATRICES;

OPTIMIZATION;

EID: 84873385216     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2012.2200900     Document Type: Article

References (47)

1. G. D. Wu and P. H. Huang, "A maximizing-discriminability-based selforganizing fuzzy network for classification problems," IEEE Trans. Fuzzy Syst., vol. 18, no. 2, pp. 362-373, Apr. 2010.
2. D. H. Lee, J. B. Park, and Y. H. Joo, "Approaches to extended nonquadratic stability and stabilization conditions for discrete-time Takagi-Sugeno fuzzy systems," Automatica, vol. 47, no. 3, pp. 534-538, 2011.
3. D. H. Lee, J. B. Park, and Y. H. Joo, "A new fuzzy Lyapunov function for relaxed stability condition of continuous-time Takagi-Sugeno fuzzy systems," IEEE Trans. Fuzzy Syst., vol. 19, no. 4, pp. 785-791, Aug. 2011.
4. Y.Wang, D.Wang, and T. Chai, "Extraction and adaptation of fuzzy rules for frictionmodeling and control compensation," IEEE Trans. Fuzzy Syst., vol. 19, no. 4, pp. 682-693, Aug. 2011.
5. X. Hong and S. Chen, "A new RBF neural network with boundary value constraints," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 39, no. 1, pp. 298-303, Feb. 2009.
6. X. Yuan, Y. Wang, W. Sun, and L. Wu, "RBF networks-based adaptive inversemodel control system for electronic throttle," IEEE Trans. Control Syst. Technol., vol. 18, no. 3, pp. 750-756, May 2010.
7. P. Angelov, D. Filev, and N. Kasabov, "Guest editorial evolving fuzzy systems-Preface to the special section," IEEE Trans. Fuzzy Syst., vol. 16, no. 6, pp. 1390-1392, Dec. 2008.
8. C. J. Mantas and J. M. Puche, "Artificial neural networks are zero-order TSK fuzzy systems," IEEE Trans. Fuzzy Syst., vol. 16, no. 3, pp. 630-643, Jun. 2008.
9. B. Pizzileo and K. Li, "A new fast algorithm for fuzzy rule selection," in Proc. Int. Conf. IEEE Fuzzy Syst., 2007, pp. 1-6.
10. 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.
11. E. H. Mamdani and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," Int. J. Man-Machine Studies, vol. 7, no. 1, pp. 1-13, 1975.
12. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Trans. Syst. Man Cybern., vol. SMC-15, no. 1, pp. 116-132, Jan./Feb. 1985.
13. A. Banakar and M. F. Azeem, "Parameter identification of TSK neurofuzzy models," Fuzzy Sets Syst., vol. 179, no. 1, pp. 62-82, 2011.
14. C. F. Juang, R. B. Huang, and W. Y. Cheng, "An interval type-2 fuzzyneural network with support-vector regression for noisy regression problems," IEEE Trans. Fuzzy Syst., vol. 18, no. 4, pp. 686-699, Aug. 2010.
15. B. Jiang, Z. Gao, P. Shi, and Y. Xu, "Adaptive fault-tolerant tracking control of near-space vehicle using Takagi-Sugeno fuzzy models," IEEE Trans. Fuzzy Syst., vol. 18, no. 5, pp. 1000-1007, Oct. 2010.
16. Z. Lendek, R. Babuska, and B. De Schutter, "Stability of cascaded fuzzy systems and observers," IEEE Trans. Fuzzy Syst., vol. 17, no. 3, pp. 641-653, Jun. 2009.
17. S. M. Zhou and J. Q. Gan, "Low-level interpretability and high-level interpretability: A unified view of data-driven interpretable fuzzy system modelling," Fuzzy Sets Syst., vol. 159, no. 23, pp. 3091-3131, 2008.
18. M. F. Azeem,M. Hanmandlu, and N. Ahmad, "Generalization of adaptive neuro-fuzzy inference systems," IEEE Trans. Neural Netw., vol. 11, no. 6, pp. 1332-1346, Nov. 2000.
19. R. Babus?ka, Fuzzy Modeling for Control. Norwell, MA: Kluwer, 1998.
20. S. Chiu, "Fuzzy model identification based on cluster estimation," J. Intell. Fuzzy Syst., vol. 2, no. 3, pp. 267-278, 1994.
21. J. C. Bezdek, Pattern Recognition With Fuzzy Objective Function Algorithms. New York: Plenum, 1981.
22. C. S. Ouyang,W. J. Lee, and S. J. Lee, "A TSK-type neurofuzzy network approach to system modeling problems," IEEE Trans. Syst.,Man, Cybern. B, Cybern., vol. 35, no. 4, pp. 751-767, Aug. 2005.
23. E. Lughofer and S. Kindermann, "SparseFIS: Data-driven learning of fuzzy systems with sparsity constraints," IEEE Trans. Fuzzy Syst., vol. 18, no. 2, pp. 396-411, Apr. 2010.
24. L. H. Chen and C. C. Hsueh, "Fuzzy regression models using the leastsquares method based on the concept of distance," IEEE Trans. Fuzzy Syst., vol. 17, no. 6, pp. 1259-1272, Dec. 2009.
25. J. S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665-685, May/Jun. 1993.
26. M. A. Shoorehdeli, M. Teshnehlab, and A. K. Sedigh, "Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter," Fuzzy Sets Syst., vol. 160, no. 7, pp. 922-948, 2009.
27. O. Nelles, Nonlinear System Identification. Berlin, Germany: Springer, 2001.
28. C. F. Juang, C. M. Hsiao, and C. H. Hsu, "Hierarchical cluster-based multispecies particle-swarm optimization for fuzzy-system optimization," IEEE Trans. Fuzzy Syst., vol. 18, no. 1, pp. 14-26, Feb. 2010.
29. C. F. Juang and P. H. Chang, "Designing fuzzy-rule-based systems using continuous ant-colony optimization," IEEE Trans. Fuzzy Syst., vol. 18, no. 1, pp. 138-149, Feb. 2010.
30. P. P. Angelov and D. P. Filev, "An approach to online identification of Takagi-Sugeno fuzzy models," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 1, pp. 484-498, Feb. 2004.
31. P. Angelov, E. Lughofer, and X. Zhou, "Evolving fuzzy classifiers using differentmodel architectures," Fuzzy Sets Syst., vol. 159, no. 23, pp. 3160-3182, 2008.
32. J. Yen, L. Wang, and C. W. Gillespie, "Improving the interpretability of TSK fuzzy models by combining global learning and local learning," IEEE Trans. Fuzzy Syst., vol. 6, no. 4, pp. 530-537, Nov 1998.
33. T. A. Johansen and R. Babuska, "Multiobjective identification of Takagi-Sugeno fuzzy models," IEEE Trans. Fuzzy Syst., vol. 11, no. 6, pp. 847-860, Dec. 2003.
34. S.-B. Roh, T.-C. Ahn, and W. Pedrycz, "The design methodology of radial basis function neural networks based on fuzzy k-nearest neighbors approach," Fuzzy Sets Syst., vol. 161, no. 13, pp. 1803-1822, 2010.
35. N. Ampazis and S. Perantonis, "Two highly efficient second-order algorithms for training feedforward networks," IEEE Trans. Neural Netw., vol. 13, no. 5, pp. 1064-1074, Sep. 2002.
36. A. V. Mulders, J. Schoukens, M. Volckaert, andM. Diehl, "Two nonlinear optimization methods for black box identification compared," Automatica, vol. 46, no. 10, pp. 1675-1681, 2010.
37. K. Li, J. X. Peng, and G. W. Irwin, "A fast nonlinear model identification method," IEEE Trans. Autom. Control, vol. 50, no. 8, pp. 1211-1216, Aug. 2005.
38. J. X. Peng, K. Li, and G.W. Irwin, "A novel continuous forward algorithm for RBF neural modelling," IEEE Trans. Autom. Control, vol. 52, no. 1, pp. 117-122, Jan. 2007.
39. J. X. Peng, K. Li, and G. W. Irwin, "A new Jacobian matrix for optimal learning of single-layer neural networks," IEEE Trans. Neural Netw., vol. 19, no. 1, pp. 119-129, Jan. 2008.
40. M. J. Gacto, R. Alcaĺa, and F. Herrera, "Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures," Inf. Sci., vol. 181, no. 20, pp. 4340-4360, 2011.
41. S. Guillaume, "Designing fuzzy inference systems from data: An interpretability-oriented review," IEEE Trans. Fuzzy Syst., vol. 9, no. 3, pp. 426-443, Jun. 2001.
42. J. Casillas, O. Cord́on, F. Herrera, and L. Magdalena, Interpretability Issues in Fuzzy Modeling. Berlin, Germany: Springer Verlag, 2003.
43. A. Fiordaliso, "A nonlinear forecasts combination method based on Takagi-Sugeno fuzzy systems," Int. J. Forecasting, vol. 14, no. 3, pp. 367-379, 1998.
44. O. Cord́on and F. Herrera, "Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rulebased systems," Fuzzy Sets Syst., vol. 118, no. 2, pp. 235-255, 2001.
45. G. Leng, T. M. McGinnity, and G. Prasad, "Design for self-organizing fuzzy neural networks based on genetic algorithms," IEEE Trans. Fuzzy Syst., vol. 14, no. 6, pp. 755-766, Dec. 2006.
46. Y. H. Chen, B. Yang, A. Abraham, and L. Z. Peng, "Automatic design of hierarchical Takagi-Sugeno type fuzzy systems using evolutionary algorithms," IEEE Trans. Fuzzy Syst., vol. 15, no. 3, pp. 385-397, Jun. 2007.
47. A. Frank and A. Asuncion. (2010). UCI machine learning repository [Online]. Available: http://archive.ics.uci.edu/ml