Input space selective fuzzification in intuitionistic semi fuzzy-neural network

Margarita Terziyska, Yancho Todorov, Marius Olteanu

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-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
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)978-150902046-1
DOIs
Publication statusPublished - 21 Feb 2017
MoE publication typeA4 Article in a conference publication
Event8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016 - Ploiesti, Romania
Duration: 30 Jun 20162 Jul 2016

Conference

Conference8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016
CountryRomania
CityPloiesti
Period30/06/162/07/16

Fingerprint

Fuzzy neural networks
learning
Chaotic systems
Fuzzy rules
Fuzzy logic
Time series
descent
Glass
logic
gradients
glass
Experiments

Keywords

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

Cite this

Terziyska, M., Todorov, Y., & Olteanu, M. (2017). Input space selective fuzzification in intuitionistic semi fuzzy-neural network. In Proceedings of the 8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016 [7861093] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECAI.2016.7861093
Terziyska, Margarita ; Todorov, Yancho ; Olteanu, Marius. / Input space selective fuzzification in intuitionistic semi fuzzy-neural network. Proceedings of the 8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016. Institute of Electrical and Electronics Engineers Inc., 2017.
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Terziyska, M, Todorov, Y & Olteanu, M 2017, Input space selective fuzzification in intuitionistic semi fuzzy-neural network. in Proceedings of the 8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016., 7861093, Institute of Electrical and Electronics Engineers Inc., 8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016, Ploiesti, Romania, 30/06/16. https://doi.org/10.1109/ECAI.2016.7861093

Input space selective fuzzification in intuitionistic semi fuzzy-neural network. / Terziyska, Margarita; Todorov, Yancho; Olteanu, Marius.

Proceedings of the 8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7861093.

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

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Terziyska M, Todorov Y, Olteanu M. Input space selective fuzzification in intuitionistic semi fuzzy-neural network. In Proceedings of the 8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7861093 https://doi.org/10.1109/ECAI.2016.7861093