Neuro-fuzzy identification of nonlinear dependencies

Authors

Avazjon Marakhimov, National University of UzbekistanFollow
Kabul Khudaybergenov, National University of UzbekistanFollow

Abstract

The paper proposes a fuzzy multilayer perceptron (MLP) and a modified algorithm for its training for solving problems of identification of nonlinear dependencies. The obtained results show a sharp reduction in the search for the optimal parameters of the neuro-fuzzy model compared to classical MLP and increase its accuracy. In the work, questions of optimization of the rule base of the neuro-fuzzy model are also investigated and the temporal and spatial complexity of the proposed algorithm is analyzed. The results of computational experiments show that the number of training epochs has sharply decreased, and productivity has increased compared to the well-known MLP models.

First Page

78

Last Page

88

References

1. Haykin S. Neural Networks: A Comprehensive Foundation (2nd Edition). Prentice Hall, (1999). – 842 p.

2. Yen J., Wang L., Gillespie C.W. Improving the interpretability of TSK fuzzy models by combining global learning with local learning. IEEE Transactions on Fuzzy Systems, Vol. 6, Issue 4, 530–537 (1998).

3. Nikov A., Georgiev T. A fuzzy neural network and its matlab simulation. Proceedings of ITI99 21st International Conference on Information Technology Interfaces, Pula, Croatia, June 15-18, 413–418 (1999).

4. Rotshtein A.P. Design and tuning of fuzzy if-then rules for medical diagnosis. Fuzzy and Neural-Fuzzy Systems in Medical and Biomedical Engineering. CRC Press, 35–97, (1998).

5. Rotshtein A.P., Shtovba S.D. Identification of a nonlinear dependence by a fuzzy knowledgebase in the case of a fuzzy training set. Cybernetics and Systems Analysis, Vol. 42, Issue 2, 176–182 (2006).

6. Setnes M., Babuska R., Kaymak U., van Nauta Lemke H.R. Similarity measures in fuzzy rule base simplification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 28, Issue 3, 376–386 (1998).

7. Zimmermann H.J. Fuzzy Set Theory and Its Applications. Kluwer, Academic Publisher, Dordrecht, Boston, MA, 2nd ed., (1991). – 315 p.

8. Borisov B., Kruglov B., Fedulov A. Fuzzy models and networks, 2-edition. Goryachays, (2012). – 284 p.

9. Mityushkin Yu.I., Mokin B.I., Rotshtein A.P. Soft Computing: identification of patterns by fuzzy knowledge bases. UNIVERSUM, Vinnytsia, 2002. – 145 p.

10. Pegat A. Fuzzy modeling and control, trans. from English 2nd ed. Beanom. Knowledge lab, Moscow, (2013). – 798 p.

11. Tarek T.A. Parametric system identification using neural networks. Applied Soft Computing, Vol. 47, 251–261 (2016).

12. Ganesh K., Chellasamy R., Deepa S.N. Formation of Fuzzy if-then Rules and Membership Function using Enhanced Particle Swarm Optimization. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 21, No. 1, 103–126 (2013).

Recommended Citation

Marakhimov, Avazjon and Khudaybergenov, Kabul (2018) "Neuro-fuzzy identification of nonlinear dependencies," Bulletin of National University of Uzbekistan: Mathematics and Natural Sciences: Vol. 1: Iss. 3, Article 1.
DOI: https://doi.org/10.56017/2181-1318.1020