International Journal of Applied Science and Engineering

Published by Chaoyang University of Technology

Huiqin Jiangab, Rung-Ching Chenb*, Qiao-En Liub and Su-Wen Huangb

aDepartment of Information Engineering, Xiamen University of Technology Xiamen, China
bDepartment of Information Management, Chaoyang University of Technology Taichung, Taiwan (R.O.C.)


 

Download Citation: |
Download PDF


ABSTRACT


With high-dimensional data appearing, the number of fuzzy rules increases which degrade the interpretability and increases the computation complexity of the fuzzy rule-based system. In this paper, we proposed a rule-reduced algorithm. Through the sparse encoding of the fuzzy basis functions (FBFs), rules are reduced. Least angle regression algorithm is proposed here to select the important rules. Compared with other sparse encoding algorithm, Least angle regression algorithm has the advantage of lower computation complexity and better performance. The experimental results show that our proposed algorithm has excellent performance, especially for high-dimension data.


Keywords: Data-driven FISs; lasso; LARS; FCM; sparse enconding.


Share this article with your colleagues

 


REFERENCES


  1. [1] Ji, R., Yang, Y. P. and Zhang, W. D. Zhang, 2014. TS-fuzzy modeling based on
    ε-insensitive smooth support vector regression. Intell. Fuzzy Syst, 24, 4:805–817.

  2. [2] Su, M. T., Chen, C. H. and Jian, C. 2011. A rule-based symbiotic modified differential evolution for self-organizing neuro-fuzzy systems. Soft Computing, 11, 8:4847–4858.

  3. [3] Gerardo, M. L. M. et al. 2014. Orthogonal-least-squares and backpropagation hybrid learning algorithm for interval A2-C1 singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems. J. Hybrid Intell. Syst, 11, 2:125–135.

  4. [4] Hu, X. S., Li, E. and Yang, Y. 2017. Advanced machine learning approach for lithiumion battery state estimation in an electric vehicle. IEEE Trans. on Transportation Electrification, 2, 2:140-149.

  5. [5] Wang, L. X. 1992. Fuzzy systems are universal approximators, in Proc. IEEE Int. Conf. On Fuzzy Systems, San Diego, 1163-1169.

  6. [6] Buckley, J. J. 1993. Sugeno type controllers are universal controllers. Fuzzy Sets and Systems, 53:299-304.

  7. [7] Castro, J. L. 1995. Fuzzy logic controllers are universal approximators. IEEE Trans. On SMC, 25:629-635.

  8. [8] Lughofer, E. D. 2008, FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models [J]. IEEE Transactions on Fuzzy Systems, 16, 6:1393-1410.

  9. [9] Chen, C. C. and Wong, C. C. 2005. Significant fuzzy rules extraction by an SVD-QR-based approach. Syst., 36, 6: 597–622.

  10. [10] Efe, M. O. and Kaynak, O. 2001. A novel optimization procedure for the training of fuzzy inference systems by combining variable structure systems technique and Levenberg–Marquardt algorithm. Fuzzy Sets Syst, 122, 1:153–165.

  11. [11] Gerardoet, M. L. M. al., 2014, Orthogonal-least-squares and backpropagation hybrid learning algorithm for interval A2-C1 singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems. J. Hybrid Intell. Syst, 11, 2:125–135.

  12. [12] Wang, L. X. 1999, Analysis and design of hierarchical fuzzy systems. IEEE Trans. Fuzzy Syst, 7, 5:617–624.

  13. [13] Yen, J. and Wang, L. 1999. Simplifying fuzzy rule-based models using orthogonal transformation methods. IEEE Transactions on Systems, Man and Cybernetics, 29, 1:13-24.

  14. [14] Jakubek, S., Hametner. C. and Keuth N. 2008. Total least squares in fuzzy system identification: an ap­plication to an industrial engine. Engineering Applications of Artificial Intelligence, 21, 8:1277-1288.

  15. [15] Destercke, S., Guillaume, S. and Charnomordic, B. 2007. Building an interpretable fuzzy rule base from data using orthogonal least squares: application to a depollution problem. Fuzzy Sets and Systems, 158, 18:2078-2094.

  16. [16] Szeidl, L. and Varlaki, P. 2009. HOSVD based canonical form for polytopic models of dynamic systems. Journal of Advanced Computational Intelligence and Intelligent Informatics, 13, 1:52- 60.

  17. [17] Tikk, D. and Baranyi, P. 2000. Comprehensive analysis of a new fuzzy rule interpolation method, IEEE Transactions on Fuzzy Systems, 8, 3:281-296.

  18. [18] Baranyi, P., Koczy, L. T. and Gedeon, T. D. 2004. A generalized concept for fuzzy rule interpolation. IEEE Transactions on Fuzzy Systems, 12, 6:820-832.

  19. [19] Ishibuchi, H., Nozaki, K., Yamamoto, N. and Tanaka, H. Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Transactions on Fuzzy Systems, 3, 3:260–270.

  20. [20] Cordón, O., Jesús, M. J., Herrera, F. and Lozano, M. MOGUL: a methodology to obtain genetic fuzzy rule-based systems under the iterative rule learning approach. International Journal of Intelligent Systems. 14, 11:1123–1153.

  21. [21] Eiben, E., Smith, J. E. 2003. Introduction to Evolutionary Computing. Springer-Verlag.

  22. [22] Cordón, and Herrera, F. 1997. A three-stage evolutionary process for learning descriptive and approximate fuzzy logic controller knowledge bases from examples. International Journal of Approximate Reasoning, 17, 4:369–407.

  23. [23] Gorzałczany, M. B. and Rudzinski, F. 2016. A multi-objective genetic optimization for fast, fuzzy rule-based credit clasfication with balanced accuracy and interpretability. Soft Comput, 40:206–220.

  24. [24] Antonelli, M., Ducange, P. and Marcelloni, F. 2014. A fast and efficient multiobjective evolutionary learning scheme for fuzzy rule-based classifiers, Sci, 283:36–54.

  25. [25] Juang,C. F., Jeng, T. L. and Chang, Y. C. 2016. An interpretable fuzzy system learned through online rule generation and multiobjective ACO with a mobile robot control application. IEEE Trans. Cybern, 46, 12 :2706–2718.

  26. [26] Dutu, L. C., Mauris, G. and Bolon, P. 2018. A Fast and Accurate Rule-Base Generation Method. IEEE Tran, On Fuzzy Systems, 26, 2:715-731.

  27. [27] Lughofer, K., and Kindermann, S. 2010. SparseFIS: Data-driven learning of fuzzy systems with sparse constraints. IEEE Trans. Fuzzy Syst, 18, 2:396–411.

  28. [28] Luo, M. N., Sun, F. and Liu, H. 2014. Hierarchical Structured Sparse Representation for T-S Fuzzy Systems Identification. IEEE Trans. On Fuzzy Systems, 21, 6:1032-1043.

  29. [29] Tropp, J. A. 2004, Greed is good: Algorithmic results for sparse approximation. IEEE Trans. Inf. Theory, 50, 10:2231–2242.

  30. [30] Tropp, A. and Gilbert, A. C. 2005. Signal recovery from partial information by orthogonal matching pursuit. unpublished manuscript.

  31. [31] Donoho, D. L. 2006. For most large underdetermined systems of linear equations, the minimal solution is also the sparsest solution. Pure Appl. Math, 59, 7:907–934.

  32. [32] Chen, S. S., Donoho, D. L. and Saunders, M. A. 1999. Atomic decomposition by basis pursuit. SIAM J. Sci Comp, 20, 1:33–61.

  33. [33] Donoho, D. L. and Tsaig, Y. 2008. Fast solution of ℓ1-norm minimization problems when the solution may be sparse. IEEE Trans. Inf. Theory, 54, 11:4789–4812.

  34. [34] Efron, B., Hastie, T., Johnstone, I. and Tibshirani, R. 2004. Least Angle Regression. The Annals of Statistics, 32, 2:407-45.


ARTICLE INFORMATION


Received: 2019-07-30
Revised: 2019-09-11
Accepted: 2019-10-21
Publication Date: 2019-11-01


Cite this article:

Jiang, H., Chen, R.C., Liu, Q.E., Huang, S.W. 2019. Fuzzy rules reduction based on sparse coding. International Journal of Applied Science and Engineering, 16, 215-227. https://doi.org/10.6703/IJASE.201911_16(3).215


We use cookies on this website to improve your user experience. By using this site you agree to its use of cookies.