127
Views
7
CrossRef citations to date
0
Altmetric
ORIGINAL ARTICLE

Extracting Interpretable Fuzzy Models for Nonlinear Systems Using Gradient-based Continuous Ant Colony Optimization

&
Pages 255-277 | Received 15 Oct 2012, Accepted 10 Apr 2013, Published online: 25 Jan 2019

References

  • Eksin I, Erol O K (2000) A fuzzy identification method for nonlinear systems. Turkish Journal of Electrical Engineering & Computer Sciences 8(2): 125–135
  • Jang J S R (1993) ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics 23(3): 665–685
  • Sanchez L, Couso I, Corrales J A (2001) Combining GP operators with SA search to evolve fuzzy rule based classifiers. Information Sciences 136: 175–191
  • Casillas J, Cordon O, Herrera F, Magdalena L (2003) Accuracy improvements in linguistic fuzzy modeling. Berlin: Springer
  • Paiva R P, Dourado A (2004) Interpretability and learning in neuro-fuzzy systems. Fuzzy Sets and Systems 147(1): 17–38
  • Jin Y, Sendhoff B (2003) Extracting interpretable fuzzy rules from RBF networks. Neural Processing Letters 17(2): 149–164
  • Wang H, Kwong S, Jin Y, Wei W, Man K F (2005) Agent-based evolutionary approach for interpretable rule-based knowledge extraction. IEEE Transactions on Systems, Man, and Cybernetics-Part C 35(2): 143–155
  • Wang H, Kwong S, Jin Y, Wei W, Man K F (2005) Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction. Fuzzy Sets and Systems 149(1): 149–186
  • Chiu S L (1994) Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems 2: 267–278
  • Eftekhari M, Katebi S D, Karimi M, Jahanmiri A H (2008) Eliciting transparent fuzzy model using differential evolution. Applied Soft Computing 8(1): 466–476
  • Eftekhari M, Katebi S D (2008) Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter. Applied Mathematical Modeling 32(12): 2634–2651
  • Eftekhari M, Majidi M, Nezamabadi P H (2012) Securing interpretability of fuzzy models for modeling nonlinear MIMO systems using a hybrid of evolutionary algorithms. Iranian Journal of Fuzzy Systems 9(1): 61–77
  • Zhou S M, Gan J Q (2008) Low-level interpretability and high-level interpretability: A unified view of data-driven interpretable fuzzy system modeling. Fuzzy Sets and Systems 159(23): 3091–3131
  • Eftekhari M, Daei B, Katebi S D (2006) Gradient-based ant colony optimization for continuous spaces. Esteghlal Journal of Eng. 25(1): 33–45
  • Eftekhari M, Moosavi MR, Katebi S D (2006) Solving constrained continuous optimization problems with GCACO II. 11th Annual Conference of Computer Society of Iran: 180–188
  • Abonyi J (2003) Fuzzy model identification for control. Boston: Birkhauser
  • Abonyi J, Babuska R, Szeifert F (2002) Modified gath-geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models. IEEE Transactions on Systems, Man, and Cybernetics-Part B 32(5): 612–621
  • Feil B, Abonyi J, Madar J, Nemeth S, Arva P (2004) Identification and analysis of MIMO systems based on clustering algorithm. Acta Agraria Kaposvariensis 8(3): 191–203
  • Herrera F (2008) Genetic fuzzy systems: taxonomy, current research trends and prospects. Evolutionary Intelligence 1(1): 27–46
  • Alonso S, Cordon O, Fernandez de Viana I, Herrera F (2004) Integrating evolutionary computation components in ant colony optimization evolutionary algorithms: an experimental study. In: L. Nunes de Castro, F. J. Von Zuben (Eds.), Recent Developments in Biologically Inspired Computing, Idea Group Publishing
  • Bilchev G, Parmee I C (1995) The ant colony metaphor for searching continuous design spaces. Lecture Notes in Computer Science 993: 25–39
  • Wodrich M, Bilchev G (1997) Cooperative distributed search: The ants way. Control and Cybernetics 26(3): 413–445
  • Socha K, Dorigo M (2008) Ant colony optimization for continuous domains. European Journal of Operational Research 185: 1155–1173
  • Michalewicz Z, Fogel D B (2005) How to solve it: Modern heuristics. Berlin: Springer Verlag
  • Dubois D J, Prade H M (1980) Fuzzy sets and systems: Theory and applications. New York: Academic Press
  • Chao C T, Chen Y J, Teng C C (1996) Simplification of fuzzy-neural systems using similarity analysis. IEEE Transactions on Systems, Man, and Cybernetics-Part B 26(2): 344–354
  • Rodriguez-Vazquez K (1999) Multiobjective evolutionary algorithms in non-linear system identification. Ph. D Thesis, Department of Automatic Control and Systems Engineering, The University of Sheffield
  • Moor B D (2010) DaISy: Database for the identification of systems. Department of Electrical Engineering, ESAT/SISTA, K. U. Leuven, Belgium. http://www.esat.kuleuven.ac.be/sista/daisy

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.