Efficient Error Based Metrics for Fuzzy-Neural Network Performance Evaluation

Margarita Terziyska, Yancho Todorov*, Maria Dobreva

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

6 Citations (Scopus)

Abstract

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 Intuitionistic 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 - 11th Annual Meeting of the Bulgarian Section of SIAM, Revised Selected Papers
EditorsMichail Todorov, Ivan Georgiev, Krassimir Georgiev, Ivan Georgiev
PublisherSpringer
Pages185-201
Number of pages17
ISBN (Print)978-331965529-1
DOIs
Publication statusPublished - 1 Jan 2018
MoE publication typeA3 Part of a book or another research book
Event11th Annual Meeting of the Bulgarian Section of the Society for Industrial and Applied Mathematics, BGSIAM 2016 - Sofia, Bulgaria
Duration: 20 Dec 201622 Dec 2016

Publication series

SeriesStudies in Computational Intelligence
Volume728
ISSN1860-949X

Conference

Conference11th Annual Meeting of the Bulgarian Section of the Society for Industrial and Applied Mathematics, BGSIAM 2016
Country/TerritoryBulgaria
CitySofia
Period20/12/1622/12/16

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