Intuitionistic fuzzy radial basis functions network for modeling of nonlinear dynamics

Yancho Todorov; Petia Koprinkova-Hristova; Margarita Terziyska

doi:10.1109/PC.2017.7976249

Intuitionistic fuzzy radial basis functions network for modeling of nonlinear dynamics

Yancho Todorov, Petia Koprinkova-Hristova, Margarita Terziyska

Not published at VTT

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

6 Citations (Scopus)

Abstract

This paper deals with a design methodology for a neural network with improved robust qualities in notion to handling uncertain input data space variations. The proposed network topology combines the simplicity of the radial basis functions networks to interpret or classify data pairs and the abilities of the intuitionistic fuzzy logic to deal with the vagueness of the data space. A simplified gradient optimization procedure as a learning approach for the designed hybrid neural network is proposed. To investigate the effects of the generated structure throughout varying network parameters, the modeling of a two benchmark chaotic time series-Mackey-Glass and Rossler under uncertain conditions is investigated. The obtained results prove the flexibility of the approach and its potentials to cope with data variations.

Original language	English
Title of host publication	Proceedings of the 2017 21st International Conference on Process Control, PC 2017
Editors	M. Kvasnica, M. Fikar
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	410-415
Number of pages	6
ISBN (Electronic)	978-1-5386-4011-1
DOIs	https://doi.org/10.1109/PC.2017.7976249
Publication status	Published - 11 Jul 2017
MoE publication type	A4 Article in a conference publication
Event	21st International Conference on Process Control, PC 2017 - Strbske Pleso, Slovakia Duration: 6 Jun 2017 → 9 Jun 2017

Conference

Conference	21st International Conference on Process Control, PC 2017
Country/Territory	Slovakia
City	Strbske Pleso
Period	6/06/17 → 9/06/17

Keywords

chaotic time series
intuitionistic fuzzy logic
neural networks
Radial Basis Functions Network
uncertanties

Access to Document

10.1109/PC.2017.7976249

Cite this

Todorov, Y., Koprinkova-Hristova, P., & Terziyska, M. (2017). Intuitionistic fuzzy radial basis functions network for modeling of nonlinear dynamics. In M. Kvasnica, & M. Fikar (Eds.), Proceedings of the 2017 21st International Conference on Process Control, PC 2017 (pp. 410-415). Article 7976249 IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/PC.2017.7976249

@inproceedings{04dcc1001fcd44c288a1114dc0c7285a,

title = "Intuitionistic fuzzy radial basis functions network for modeling of nonlinear dynamics",

abstract = "This paper deals with a design methodology for a neural network with improved robust qualities in notion to handling uncertain input data space variations. The proposed network topology combines the simplicity of the radial basis functions networks to interpret or classify data pairs and the abilities of the intuitionistic fuzzy logic to deal with the vagueness of the data space. A simplified gradient optimization procedure as a learning approach for the designed hybrid neural network is proposed. To investigate the effects of the generated structure throughout varying network parameters, the modeling of a two benchmark chaotic time series-Mackey-Glass and Rossler under uncertain conditions is investigated. The obtained results prove the flexibility of the approach and its potentials to cope with data variations.",

keywords = "chaotic time series, intuitionistic fuzzy logic, neural networks, Radial Basis Functions Network, uncertanties",

author = "Yancho Todorov and Petia Koprinkova-Hristova and Margarita Terziyska",

year = "2017",

month = jul,

day = "11",

doi = "10.1109/PC.2017.7976249",

language = "English",

pages = "410--415",

editor = "M. Kvasnica and M. Fikar",

booktitle = "Proceedings of the 2017 21st International Conference on Process Control, PC 2017",

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

address = "United States",

note = "21st International Conference on Process Control, PC 2017 ; Conference date: 06-06-2017 Through 09-06-2017",

}

Todorov, Y, Koprinkova-Hristova, P & Terziyska, M 2017, Intuitionistic fuzzy radial basis functions network for modeling of nonlinear dynamics. in M Kvasnica & M Fikar (eds), Proceedings of the 2017 21st International Conference on Process Control, PC 2017., 7976249, IEEE Institute of Electrical and Electronic Engineers, pp. 410-415, 21st International Conference on Process Control, PC 2017, Strbske Pleso, Slovakia, 6/06/17. https://doi.org/10.1109/PC.2017.7976249

Intuitionistic fuzzy radial basis functions network for modeling of nonlinear dynamics. / Todorov, Yancho; Koprinkova-Hristova, Petia; Terziyska, Margarita.
Proceedings of the 2017 21st International Conference on Process Control, PC 2017. ed. / M. Kvasnica; M. Fikar. IEEE Institute of Electrical and Electronic Engineers, 2017. p. 410-415 7976249.

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

TY - GEN

T1 - Intuitionistic fuzzy radial basis functions network for modeling of nonlinear dynamics

AU - Todorov, Yancho

AU - Koprinkova-Hristova, Petia

AU - Terziyska, Margarita

PY - 2017/7/11

Y1 - 2017/7/11

N2 - This paper deals with a design methodology for a neural network with improved robust qualities in notion to handling uncertain input data space variations. The proposed network topology combines the simplicity of the radial basis functions networks to interpret or classify data pairs and the abilities of the intuitionistic fuzzy logic to deal with the vagueness of the data space. A simplified gradient optimization procedure as a learning approach for the designed hybrid neural network is proposed. To investigate the effects of the generated structure throughout varying network parameters, the modeling of a two benchmark chaotic time series-Mackey-Glass and Rossler under uncertain conditions is investigated. The obtained results prove the flexibility of the approach and its potentials to cope with data variations.

AB - This paper deals with a design methodology for a neural network with improved robust qualities in notion to handling uncertain input data space variations. The proposed network topology combines the simplicity of the radial basis functions networks to interpret or classify data pairs and the abilities of the intuitionistic fuzzy logic to deal with the vagueness of the data space. A simplified gradient optimization procedure as a learning approach for the designed hybrid neural network is proposed. To investigate the effects of the generated structure throughout varying network parameters, the modeling of a two benchmark chaotic time series-Mackey-Glass and Rossler under uncertain conditions is investigated. The obtained results prove the flexibility of the approach and its potentials to cope with data variations.

KW - chaotic time series

KW - intuitionistic fuzzy logic

KW - neural networks

KW - Radial Basis Functions Network

KW - uncertanties

UR - http://www.scopus.com/inward/record.url?scp=85027525290&partnerID=8YFLogxK

U2 - 10.1109/PC.2017.7976249

DO - 10.1109/PC.2017.7976249

M3 - Conference article in proceedings

AN - SCOPUS:85027525290

SP - 410

EP - 415

BT - Proceedings of the 2017 21st International Conference on Process Control, PC 2017

A2 - Kvasnica, M.

A2 - Fikar, M.

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 21st International Conference on Process Control, PC 2017

Y2 - 6 June 2017 through 9 June 2017

ER -

Intuitionistic fuzzy radial basis functions network for modeling of nonlinear dynamics

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this