Simple heuristic approach for training of type-2 NEO-fuzzy neural network

Yancho Todorov; Margarita Terziyska

doi:10.1109/PC.2015.7169976

Simple heuristic approach for training of type-2 NEO-fuzzy neural network

Yancho Todorov, Margarita Terziyska

Not published at VTT

Bulgarian Academy of Sciences

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

1 Citation (Scopus)

Abstract

This paper describes the development of Interval Type-2 NEO-Fuzzy Neural Network for modeling of complex dynamics. The proposed network represents a parallel set of multiple zero order Sugeno type approximations, related only to their own input argument. As learning procedure a simple heuristic backpropagation approach, where the sign of the gradient is taken into account, is adopted. To improve the robustness of the network and the possibilities for handling uncertainties, Interval Type-2 Gaussian fuzzy sets are introduced into the network topology. The potentials of the proposed approach in modeling of Mackey-Glass and Rossler Chaotic time series are studied. A comparison is made with the classical Gradient Descent learning approach.

Original language	English
Title of host publication	Proceedings of the 2015 20th International Conference on Process Control, PC 2015
Editors	M. Fikar, M. Kvasnica
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	278-283
ISBN (Print)	978-1-4673-6627-4
DOIs	https://doi.org/10.1109/PC.2015.7169976
Publication status	Published - 28 Jul 2015
MoE publication type	A4 Article in a conference publication
Event	20th International Conference on Process Control, PC 2015 - Strbske Pleso, Slovakia Duration: 9 Jun 2015 → 12 Jun 2015

Conference

Conference	20th International Conference on Process Control, PC 2015
Country/Territory	Slovakia
City	Strbske Pleso
Period	9/06/15 → 12/06/15

Keywords

Chaotic time-series prediction
Dynamic modeling
Fuzzy systems
Neo-fuzzy neuron
Neural networks

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10.1109/PC.2015.7169976

Cite this

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title = "Simple heuristic approach for training of type-2 NEO-fuzzy neural network",

abstract = "This paper describes the development of Interval Type-2 NEO-Fuzzy Neural Network for modeling of complex dynamics. The proposed network represents a parallel set of multiple zero order Sugeno type approximations, related only to their own input argument. As learning procedure a simple heuristic backpropagation approach, where the sign of the gradient is taken into account, is adopted. To improve the robustness of the network and the possibilities for handling uncertainties, Interval Type-2 Gaussian fuzzy sets are introduced into the network topology. The potentials of the proposed approach in modeling of Mackey-Glass and Rossler Chaotic time series are studied. A comparison is made with the classical Gradient Descent learning approach.",

keywords = "Chaotic time-series prediction, Dynamic modeling, Fuzzy systems, Neo-fuzzy neuron, Neural networks",

author = "Yancho Todorov and Margarita Terziyska",

year = "2015",

month = jul,

day = "28",

doi = "10.1109/PC.2015.7169976",

language = "English",

isbn = "978-1-4673-6627-4",

pages = "278--283",

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

booktitle = "Proceedings of the 2015 20th International Conference on Process Control, PC 2015",

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

address = "United States",

note = "20th International Conference on Process Control, PC 2015 ; Conference date: 09-06-2015 Through 12-06-2015",

}

Todorov, Y & Terziyska, M 2015, Simple heuristic approach for training of type-2 NEO-fuzzy neural network. in M Fikar & M Kvasnica (eds), Proceedings of the 2015 20th International Conference on Process Control, PC 2015., 7169976, IEEE Institute of Electrical and Electronic Engineers, pp. 278-283, 20th International Conference on Process Control, PC 2015, Strbske Pleso, Slovakia, 9/06/15. https://doi.org/10.1109/PC.2015.7169976

Simple heuristic approach for training of type-2 NEO-fuzzy neural network. / Todorov, Yancho; Terziyska, Margarita.
Proceedings of the 2015 20th International Conference on Process Control, PC 2015. ed. / M. Fikar; M. Kvasnica. IEEE Institute of Electrical and Electronic Engineers, 2015. p. 278-283 7169976.

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

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N2 - This paper describes the development of Interval Type-2 NEO-Fuzzy Neural Network for modeling of complex dynamics. The proposed network represents a parallel set of multiple zero order Sugeno type approximations, related only to their own input argument. As learning procedure a simple heuristic backpropagation approach, where the sign of the gradient is taken into account, is adopted. To improve the robustness of the network and the possibilities for handling uncertainties, Interval Type-2 Gaussian fuzzy sets are introduced into the network topology. The potentials of the proposed approach in modeling of Mackey-Glass and Rossler Chaotic time series are studied. A comparison is made with the classical Gradient Descent learning approach.

AB - This paper describes the development of Interval Type-2 NEO-Fuzzy Neural Network for modeling of complex dynamics. The proposed network represents a parallel set of multiple zero order Sugeno type approximations, related only to their own input argument. As learning procedure a simple heuristic backpropagation approach, where the sign of the gradient is taken into account, is adopted. To improve the robustness of the network and the possibilities for handling uncertainties, Interval Type-2 Gaussian fuzzy sets are introduced into the network topology. The potentials of the proposed approach in modeling of Mackey-Glass and Rossler Chaotic time series are studied. A comparison is made with the classical Gradient Descent learning approach.

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KW - Dynamic modeling

KW - Fuzzy systems

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Simple heuristic approach for training of type-2 NEO-fuzzy neural network

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