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Reduced Rule-Base Fuzzy-Neural Networks

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Part of the Studies in Computational Intelligence book series (SCI, volume 681)

Abstract

In this paper two different fuzzy-neural systems with reduced fuzzy rules bases, namely Distributed Adaptive Neuro Fuzzy Architecture (DANFA) and Semi Fuzzy Neural Network (SFNN), are presented. Both structures are realized with Takagi-Sugeno fuzzy inference mechanism and they posses reduced number of parameters for update during the learning procedure. Thus, the computational time for algorithm execution is additionally reduced, which make the modeling structures a promising solution for real time applications. As a learning approach for the designed structures a simplified two-step gradient descent approach is implemented. To demonstrate the potentials of both models, simulation experiments with two benchmark chaotic time systems—Mackey-Glass and Rossler are studied. The obtained results show accurate models performance with minimal prediction error.

Keywords

Membership Function Fuzzy System Fuzzy Rule Learning Procedure Fuzzy Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Chennakesava, R.A.: Fuzzy Logic and Neural Networks: Basic Concepts and Applications. New Age International Pvt Ltd Publishers (2008)Google Scholar
  2. 2.
    Jang, R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(5), 665–685 (1993)CrossRefGoogle Scholar
  3. 3.
    Kasabov, N., Qun, S.: DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans. Fuzzy Syst. 10(2) (2002)Google Scholar
  4. 4.
    Chuang, K.-S., et al.: Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph. 30, 9–15 (2006)Google Scholar
  5. 5.
    Zalik, K.: Fuzzy C-Means Clustering and Facility Location Problems. Artificial Intelligence and Soft Computing (2006)Google Scholar
  6. 6.
    Gallucc, L., et al.: Graph based k-means clustering. Sign. Proces. 92(9), 1970–1984 (2012)CrossRefGoogle Scholar
  7. 7.
    Mehrabian, A.R., et al.: Neuro-fuzzy modeling of super-heating system of a steam power plant. Artif. Intell. Appl. 13–16 (2006)Google Scholar
  8. 8.
    Panella, M.: A hierarchical procedure for the synthesis of ANFIS networks. Adv. Fuzzy Syst. (2012)Google Scholar
  9. 9.
    Kasabov, N., Filev, D.: Evolving intelligent systems: methods, learning and applications. In: International Symposium of Evolving Fuzzy Systems, Sept 2006 (2006)Google Scholar
  10. 10.
    Angelov, P.: Autonomous Learning Systems. Wiley (2013)Google Scholar
  11. 11.
    Allende-Cid, H., et al.: Self-organizing neuro-fuzzy inference system. In: Iberoamerican Congress on Pattern Recognition CIARP, pp. 429–436 (2008)Google Scholar
  12. 12.
    Ferreyra, A., Rubio, J.: A new on-line self-constructing neural fuzzy network. In: Proceedings of the 45th IEEE Conference on Decision and Control, San Diego, CA, USA (2006)Google Scholar
  13. 13.
    Gegov, A.: Complexity Management in Fuzzy Systems, Studies in Fuzziness and Soft Computing, vol. 211 (2007)Google Scholar
  14. 14.
    Gegov, A., Gobalakrishnan, N.: Advanced inference in fuzzy systems by rule base compression. Mathware Soft Comput. 14, 201–216 (2007)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Informatics and StatisticsUniversity of Food TechnologiesPlovdivBulgaria
  2. 2.Institute of Information and Communication TechnologiesBulgarian Academy of SciencesSofiaBulgaria

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