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 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 language | English |
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Title of host publication | Advanced Computing in Industrial Mathematics |
Subtitle of host publication | 11th Annual Meeting of the Bulgarian Section of SIAM December 20-22, 2016, Sofia, Bulgaria. Revised Selected Papers |
Editors | Krasimir Georgiev, Michail Todorov, Ivan Georgiev |
Publisher | Springer |
Pages | 185-201 |
Number of pages | 16 |
Volume | 728 |
ISBN (Electronic) | 978-3-319-65530-7 |
ISBN (Print) | 978-3-319-65529-1 |
DOIs | |
Publication status | Published - 1 Jan 2018 |
MoE publication type | A3 Book section, Chapters in research books |
Event | Annual Meeting of the Bulgarian Section of SIAM - Sofia, Bulgaria Duration: 20 Dec 2016 → 22 Dec 2016 Conference number: 11 |
Publication series
Name | Studies in Computational Intelligence |
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Publisher | Springer |
Volume | 728 |
ISSN (Print) | 1860-949X |
ISSN (Electronic) | 1860-9503 |
Conference
Conference | Annual Meeting of the Bulgarian Section of SIAM |
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Abbreviated title | BGSIAM |
Country/Territory | Bulgaria |
City | Sofia |
Period | 20/12/2016 → 22/12/2016 |
Keywords
- artificial neural network
- modeling
- efficent metrics
- error estimates