Efficient Error Based Metrics for Fuzzy-Neural Network Performance Evaluation

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

  • Margarita Terziyska
  • Yancho Todorov
  • Maria Dobreva

Organisaatiot

  • University of Food Technologies-Plovdiv

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoAdvanced Computing in Industrial Mathematics
Alaotsikko11th Annual Meeting of the Bulgarian Section of SIAM December 20-22, 2016, Sofia, Bulgaria. Revised Selected Papers
ToimittajatKrasimir Georgiev, Michail Todorov, Ivan Georgiev
TilaJulkaistu - 1 tammikuuta 2018
OKM-julkaisutyyppiA3 Kirjan osa tai toinen tutkimuskirja
TapahtumaAnnual Meeting of the Bulgarian Section of SIAM - Sofia, Bulgaria
Kesto: 20 joulukuuta 201622 joulukuuta 2016
Konferenssinumero: 11

Julkaisusarja

NimiStudies in Computational Intelligence
KustantajaSpringer
Vuosikerta728
ISSN (painettu)1860-949X
ISSN (elektroninen)1860-9503

Conference

ConferenceAnnual Meeting of the Bulgarian Section of SIAM
LyhennettäBGSIAM
MaaBulgaria
KaupunkiSofia
Ajanjakso20/12/201622/12/2016

ID: 13371067