Input space selective fuzzification in intuitionistic semi fuzzy-neural network

Margarita Terziyska, Yancho Todorov, Marius Olteanu

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

3 Sitaatiot (Scopus)

Abstrakti

In this paper, the influence of the selective fuzzification of the input space in Intuitionistic Semi-Fuzzy Neural Network (ISFNN) is investigated. The ISFNN represents a structure modification of the classical fuzzy-neural approach where selective fuzzification as a means to reduce the number of the generated fuzzy rules is proposed, thus expected to reduce the number of the associated learning parameters and to achieve a degree of computational simplicity. On the other hand, the potentials of the network are supplemented by intuitionistic fuzzy logic, in order to handle uncertain data variations. As a learning procedure for the proposed structure, a two-step gradient descent algorithm is employed. To investigate the influence of input space fuzzificaton, several test experiments in modeling of a two benchmark chaotic systems - Mackey-Glass and Rossler chaotic time series are made.

AlkuperäiskieliEnglanti
OtsikkoProceedings of the 8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016
KustantajaIEEE
Sivut1-7
ISBN (elektroninen)9781509020461
DOI - pysyväislinkit
TilaJulkaistu - 21 helmikuuta 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Electronics, Computers and Artificial Intelligence - Ploiesti, Romania
Kesto: 30 kesäkuuta 20162 heinäkuuta 2016
Konferenssinumero: 8

Conference

ConferenceInternational Conference on Electronics, Computers and Artificial Intelligence
LyhennettäECAI
Maa/AlueRomania
KaupunkiPloiesti
Ajanjakso30/06/201602/07/2016

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