Efficient Error Based Metrics for Fuzzy-Neural Network Performance Evaluation

Margarita Terziyska, Yancho Todorov, Maria Dobreva

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

5 Citations (Scopus)


In this paper the effectiveness of different error metrics for assessment of the capabilities of an advanced fuzzy-neural architecture are studied. The proposed structure combines the potentials of the Intuitionistsc Fuzzy Logic with the simplicity of the Neo-Fuzzy Neuron theory for implementation of robust modeling mechanisms, able to capture uncertain variations in the data space. A major concern when evaluating the performance of such kind of models is the selection of appropriate error metrics in order to assess their potential to capture a wide range of system behaviours. Therefore, different error metrics to evaluate the functional properties of a proposed Intuitionistic Neo-fuzzy network are studied and a comparative analysis in modeling of chaotic time series is made.
Original languageEnglish
Title of host publicationAdvanced Computing in Industrial Mathematics
Subtitle of host publication11th Annual Meeting of the Bulgarian Section of SIAM December 20-22, 2016, Sofia, Bulgaria. Revised Selected Papers
EditorsKrasimir Georgiev, Michail Todorov, Ivan Georgiev
Number of pages16
ISBN (Electronic)978-3-319-65530-7
ISBN (Print)978-3-319-65529-1
Publication statusPublished - 1 Jan 2018
MoE publication typeA3 Part of a book or another research book
EventAnnual Meeting of the Bulgarian Section of SIAM - Sofia, Bulgaria
Duration: 20 Dec 201622 Dec 2016
Conference number: 11

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503


ConferenceAnnual Meeting of the Bulgarian Section of SIAM
Abbreviated titleBGSIAM


  • artificial neural network
  • modeling
  • efficent metrics
  • error estimates


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