KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Identification of Fuzzy Inference System Based on Information Granulation

  • Huang, Wei (State Key Laboratory of Software Engineering, Wuhan University) ;
  • Ding, Lixin (State Key Laboratory of Software Engineering, Wuhan University) ;
  • Oh, Sung-Kwun (Department of Electrical Engineering, University of Suwon) ;
  • Jeong, Chang-Won (Department of Computer Engineering, Wonkwang University) ;
  • Joo, Su-Chong (Department of Computer Engineering, Wonkwang University)
  • Received : 2010.04.19
  • Accepted : 2010.06.28
  • Published : 2010.08.27
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with "conventional" evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polyno.mial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using four representative numerical examples such as No.n-linear function, gas furnace, NO.x emission process data, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some "conventional" fuzzy models already encountered in the literature.

Keywords

References

  1. M.C. Nataraja, M.A.Jayaram, C.N.Ravikumar, "Prediction of Early Strength of Concrete: A Fuzzy Inference System Model," J. International Journal of Physical Sciences. vol. 1, pp. 47-56, 2006.
  2. W. Pedrycz, "An identification algorithm in fuzzy relational system," J. Fuzzy Sets Syst. vol. 13, pp. 153-167, 1984. https://doi.org/10.1016/0165-0114(84)90015-0
  3. R. M. Tong, "The evaluation of fuzzy models derived from experimental data," J. Fuzzy Sets Syst. vol. 13, pp 1-12, 1980.
  4. C. W. Xu., Y. Zailu, "Fuzzy model identification self-learning for dynamic system," J. IEEE Trans. on System, Man, and Cybernetics. vol. 17 no.4, pp. 683-689, 1987. https://doi.org/10.1109/TSMC.1987.289361
  5. M. Sugeno., T. Yasukawa, "Linguistic modeling based on numerical data," in proc. of IFSA'91 Brussels, Computer, Management & System Science. pp. 264-267. 1991.
  6. S. K. Oh., W. Pedrycz, "Identification of Fuzzy Systems by means of an Auto-Tuning Algorithm and Its Application to No.nlinear Systems," J. Fuzzy Sets and Syst. vol. 115 no.2, pp 205-230, 2000. https://doi.org/10.1016/S0165-0114(98)00174-2
  7. F. Liu, P. Lu, R. Pei, "A new fuzzy modeling and identification based on fast-cluster and genetic algorithm," J. Intell. Contr. Automat. vol. 1, pp. 290-293, 2004.
  8. W.Y Chung, W. Pedrycz, E.T Kim, "A new two-phase approach to fuzzy modeling for no.nlinear function approximation," J. IEICE Trans. Info. Syst. vol. 9, pp. 2473-2483, 2006.
  9. Z.-L. Gaing, "A particle swarm optimization approach for optimum design of PID controller in AVR system," J. IEEE Trans. Energy Conversion. vol. 19, pp. 384-391, 2004. https://doi.org/10.1109/TEC.2003.821821
  10. B. J. Park., W. Pedrycz., S. K. Oh, "Identification of Fuzzy Models with the Aid of Evolutionary Data Granulation," IEE Proc.-Control Theory and Applications, Vol. 148, pp. 406-418, 2001. https://doi.org/10.1049/ip-cta:20010677
  11. J.N. Choi, S.K. Oh, and W. Pedrycz, "Structural and parametric design of fuzzy inference systems using hierarchical fair competition-based parallel genetic algorithms and information granulation," International Journal of Approximate Reasoning, vol. 49, pp. 631-648, 2008. https://doi.org/10.1016/j.ijar.2008.06.006
  12. S. Horikawa, T. Furuhashi and Y. Uchigawa, "On fuzzy modeling using fuzzy neural networks with the back propagation algorithm," IEEE Trans. Neural Networks, vol. 3, no. 5, pp. 801-806, 1992. https://doi.org/10.1109/72.159069
  13. G. Kang, M. Sugeno., "Fuzzy modeling," Trans. SICE, vol. 23, no. 6, pp. 106-108, 1987.
  14. T. Kondo, "Revised GMDH algorithm estimating degree of the complete polyno.mial," Trans. Soc. Instrum. Control Eng., vol. 22, no. 9, pp. 928-934, 1986. https://doi.org/10.9746/sicetr1965.22.928
  15. S.K. Oh, W. Pedrycz, H.S. Park, "Rule-based multi-FNN identification with the aid of evolutionary fuzzy granulation," Knowledge-Based Systems, vol. 17, pp. 1-13, 2004. https://doi.org/10.1016/S0950-7051(03)00047-9
  16. S.K. Oh, W. Pedrycz, H.S. Prak, "Hybrid identification in fuzzy-neural networks," Fuzzy Set System., vol. 138, no. 2, pp. 399-426, 2003. https://doi.org/10.1016/S0165-0114(02)00441-4
  17. H.S. Park, S.K. Oh, "Fuzzy relation-based fuzzy neural-networks using a hybrid identification algorithm," Int. J. Cont., Autom. Syst., vol. 1, no. 2, pp. 289-300, 2003.
  18. H.S. Park, S.K. Oh, "Multi-FNN identification based on HCM clustering and evolutionary fuzzy granulation," Int. J. Cont., Autom. Syst., vol. 1, No. 2, pp. 194-202, 2003.
  19. S.K. Oh, W. Pedrycz, H.S. Prak, "Implicit rule-based fuzzy-neural networks using the identification algorithm of hybrid scheme based on information granulation," Adv. Eng. Inform., vol. 16, no. 4, pp. 247-263, 2002. https://doi.org/10.1016/S1474-0346(03)00016-8
  20. S.K. Oh, W. Pedrycz, "A new approach to self-organizing multi-layer fuzzy polyno.mial neural networks based on genetic optimization," Adv. Eng. Inform., vol. 18, pp. 29-39, 2004. https://doi.org/10.1016/j.aei.2004.05.001
  21. J.N. Choi, S.K. Oh, W. Pedrycz, "Identification of fuzzy relation models using hierarchical fair competition-based parallel genetic algorithms and information granulation," Applied Mathematical Modelling, vol. 33, pp. 2791-2807, 2009. https://doi.org/10.1016/j.apm.2008.08.022
  22. P.R. Krishnaiah., L.N. Kanal (Eds.), "Classification, Pattern Recognition, and Reduction of Dimensionality. Handbook of Statistics," vol. 2, No.rth-Holland, Amsterdam. 1982.
  23. L. X. Wang., J. M. Mendel, "Generating fuzzy rules from numerical data with applications," J. IEEE Trans. on System, Man, and Cybernetics. vol. 22, pp. 1414-1427, 1992. https://doi.org/10.1109/21.199466
  24. J.S.R Jang, "ANFIS: adaptive-network-based fuzzy inference system" J. IEEE Trans. System Man Cybernet., vol. 23, no. 3, pp. 665-685, 1993. https://doi.org/10.1109/21.256541
  25. L.P. Maguire, B. Roche, T.M. McGinnity, L.J. McDaid, "Predicting a chaotic time series using a fuzzy neural network," J. Inform. Sci., vol. 112, pp. 125-136, 1998. https://doi.org/10.1016/S0020-0255(98)10026-9
  26. J.C. Duan., F.-L. Chung, "Multilevel fuzzy relational systems: structure and identification," J. Soft Comput., vol. 6, pp. 71-86, 2002. https://doi.org/10.1007/s005000100144
  27. Y. Chen., B. Yang., A. Abraham, "Automatic design of hierarchical Takagi-Sugeno. type fuzzy systems using evolutionary algorithms," J. IEEE Trans. Fuzzy Systems., vol. 15, no. 3, pp. 385-397, 2007. https://doi.org/10.1109/TFUZZ.2006.882472

Cited by

  1. 공간탐색 진화알고리즘을 이용한 Interval Type-2 pRBF 뉴럴 네트워크의 구조적 해석 vol.21, pp.1, 2010, https://doi.org/10.5391/jkiis.2011.21.1.12
  2. Identification of Fuzzy Inference Systems Using a Multi-objective Space Search Algorithm and Information Granulation vol.6, pp.6, 2010, https://doi.org/10.5370/jeet.2011.6.6.853
  3. Multiobjective Space Search Optimization and Information Granulation in the Design of Fuzzy Radial Basis Function Neural Networks vol.7, pp.4, 2010, https://doi.org/10.5370/jeet.2012.7.4.636
  4. Design of Fuzzy Models with the Aid of an Improved Differential Evolution vol.22, pp.4, 2010, https://doi.org/10.5391/jkiis.2012.22.4.399
  5. Data-Driven Interval Type-2 Neural Fuzzy System With High Learning Accuracy and Improved Model Interpretability vol.43, pp.6, 2010, https://doi.org/10.1109/tsmcb.2012.2230253
  6. Identification of Fuzzy Inference Systems by Means of a Multiobjective Opposition-Based Space Search Algorithm vol.2013, pp.None, 2010, https://doi.org/10.1155/2013/725017
  7. Design of Polynomial Fuzzy Radial Basis Function Neural Networks Based on Nonsymmetric Fuzzy Clustering and Parallel Optimization vol.2013, pp.None, 2013, https://doi.org/10.1155/2013/745314