Reduced rule-base fuzzy-neural networks

Margarita Terziyska, Yancho Todorov*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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 languageEnglish
Title of host publicationAdvanced Computing in Industrial Mathematics - Revised Selected Papers of the 10th Annual Meeting of the Bulgarian Section of SIAM
Number of pages16
ISBN (Electronic)978-3-319-49544-6
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventAnnual Meeting of the Bulgarian Section of SIAM - Sofia, Bulgaria
Duration: 20 Dec 201522 Dec 2015
Conference number: 10

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860949X


ConferenceAnnual Meeting of the Bulgarian Section of SIAM
Abbreviated titleBGSIAM
City Sofia

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