Input space selective fuzzification in intuitionistic semi fuzzy-neural network

Margarita Terziyska; Yancho Todorov; Marius Olteanu

doi:10.1109/ECAI.2016.7861093

Input space selective fuzzification in intuitionistic semi fuzzy-neural network

Margarita Terziyska, Yancho Todorov, Marius Olteanu

Not published at VTT

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

3 Citations (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 language	English
Title of host publication	Proceedings of the 8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016
Publisher	IEEE Institute of Electrical and Electronic Engineers
ISBN (Electronic)	978-150902046-1
DOIs	https://doi.org/10.1109/ECAI.2016.7861093
Publication status	Published - 21 Feb 2017
MoE publication type	A4 Article in a conference publication
Event	8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016 - Ploiesti, Romania Duration: 30 Jun 2016 → 2 Jul 2016

Conference

Conference	8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016
Country/Territory	Romania
City	Ploiesti
Period	30/06/16 → 2/07/16

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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10.1109/ECAI.2016.7861093

Cite this

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title = "Input space selective fuzzification in intuitionistic semi fuzzy-neural network",

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.",

keywords = "Chaotic time series, Fuzzy-Neural Models, Intuitionistic fuzzy logic, Nonlinear Identification, Nonlinear Modelling, Semi-Fuzzy Neural Network, Takagi-Sugeno fuzzy inference",

author = "Margarita Terziyska and Yancho Todorov and Marius Olteanu",

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doi = "10.1109/ECAI.2016.7861093",

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booktitle = "Proceedings of the 8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "8th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2016 ; Conference date: 30-06-2016 Through 02-07-2016",

}

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, IEEE Institute of Electrical and Electronic Engineers, 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. IEEE Institute of Electrical and Electronic Engineers, 2017. 7861093.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

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AU - Terziyska, Margarita

AU - Todorov, Yancho

AU - Olteanu, Marius

PY - 2017/2/21

Y1 - 2017/2/21

N2 - 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.

AB - 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.

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KW - Nonlinear Identification

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ER -

Input space selective fuzzification in intuitionistic semi fuzzy-neural network

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