Input space selective fuzzification in intuitionistic semi fuzzy-neural network

Margarita Terziyska, Yancho Todorov, Marius Olteanu

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016
PublisherIEEE
Pages1-7
ISBN (Electronic)9781509020461
DOIs
Publication statusPublished - 21 Feb 2017
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Electronics, Computers and Artificial Intelligence - Ploiesti, Romania
Duration: 30 Jun 20162 Jul 2016
Conference number: 8

Conference

ConferenceInternational Conference on Electronics, Computers and Artificial Intelligence
Abbreviated titleECAI
CountryRomania
CityPloiesti
Period30/06/201602/07/2016

Keywords

  • Chaotic time series
  • Fuzzy-Neural Models
  • Intuitionistic fuzzy logic
  • Nonlinear Identification
  • Nonlinear Modelling
  • Semi-Fuzzy Neural Network
  • Takagi-Sugeno fuzzy inference

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