Reduced rule-base fuzzy-neural networks

Margarita Terziyska; Yancho Todorov

doi:10.1007/978-3-319-49544-6_17

Reduced rule-base fuzzy-neural networks

Margarita Terziyska, Yancho Todorov

Not published at VTT

Research output: Chapter in Book/Report/Conference proceeding › Chapter or book article › Scientific › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Advanced Computing in Industrial Mathematics - Revised Selected Papers of the 10th Annual Meeting of the Bulgarian Section of SIAM
Editors	Ivan Georgiev, Michail Todorov, Krassimir Georgiev
Publisher	Springer
Pages	199-214
Number of pages	16
ISBN (Print)	978-3-31949543-9
DOIs	https://doi.org/10.1007/978-3-319-49544-6_17
Publication status	Published - 1 Jan 2017
MoE publication type	A3 Part of a book or another research book
Event	10th Annual Meeting of the Bulgarian Section of SIAM, BGSIAM 2015 - Sofia, Bulgaria Duration: 21 Dec 2015 → 22 Dec 2015

Publication series

Series	Studies in Computational Intelligence
Volume	681
ISSN	1860-949X

Conference

Conference	10th Annual Meeting of the Bulgarian Section of SIAM, BGSIAM 2015
Country/Territory	Bulgaria
City	Sofia
Period	21/12/15 → 22/12/15

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10.1007/978-3-319-49544-6_17

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Terziyska, M., & Todorov, Y. (2017). Reduced rule-base fuzzy-neural networks. In I. Georgiev, M. Todorov, & K. Georgiev (Eds.), Advanced Computing in Industrial Mathematics - Revised Selected Papers of the 10th Annual Meeting of the Bulgarian Section of SIAM (pp. 199-214). Springer. Studies in Computational Intelligence Vol. 681 https://doi.org/10.1007/978-3-319-49544-6_17

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Terziyska, M & Todorov, Y 2017, Reduced rule-base fuzzy-neural networks. in I Georgiev, M Todorov & K Georgiev (eds), Advanced Computing in Industrial Mathematics - Revised Selected Papers of the 10th Annual Meeting of the Bulgarian Section of SIAM. Springer, Studies in Computational Intelligence, vol. 681, pp. 199-214, 10th Annual Meeting of the Bulgarian Section of SIAM, BGSIAM 2015, Sofia, Bulgaria, 21/12/15. https://doi.org/10.1007/978-3-319-49544-6_17

Reduced rule-base fuzzy-neural networks. / Terziyska, Margarita; Todorov, Yancho.
Advanced Computing in Industrial Mathematics - Revised Selected Papers of the 10th Annual Meeting of the Bulgarian Section of SIAM. ed. / Ivan Georgiev; Michail Todorov; Krassimir Georgiev. Springer, 2017. p. 199-214 (Studies in Computational Intelligence, Vol. 681).

Research output: Chapter in Book/Report/Conference proceeding › Chapter or book article › Scientific › peer-review

TY - CHAP

T1 - Reduced rule-base fuzzy-neural networks

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AU - Todorov, Yancho

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N2 - 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.

AB - 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.

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T2 - 10th Annual Meeting of the Bulgarian Section of SIAM, BGSIAM 2015

Y2 - 21 December 2015 through 22 December 2015

ER -

Reduced rule-base fuzzy-neural networks

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