Levenberg Marquardt algorithm for the training of type-2 fuzzy neuro systems with a novel type-2 fuzzy membership function

Mojtaba Ahmadieh Khanesar; Erdal Kayacan; Mohammad Teshnehlab; Okyay Kaynak

doi:10.1109/T2FUZZ.2011.5949558

Levenberg Marquardt algorithm for the training of type-2 fuzzy neuro systems with a novel type-2 fuzzy membership function

Mojtaba Ahmadieh Khanesar^*, Erdal Kayacan, Mohammad Teshnehlab, Okyay Kaynak

^*Corresponding author for this work

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

Abstract

A new training approach based on the Levenberg-Marquardt algorithm is proposed for type-2 fuzzy neural networks. While conventional gradient descent algorithms use only the first order derivative, the proposed algorithm used in this paper benefits from the first and the second order derivatives which makes the training procedure faster. Besides, this approach is more robust than the other techniques that use the second order derivatives, e.g. Gauss-Newton's method. The training algorithm proposed is tested on the training of a type-2 fuzzy neural network used for the prediction of a chaotic Mackey-Glass time series. The results show that the learning algorithm proposed not only results in faster training but also in a better forecasting accuracy.

Original language	English
Title of host publication	IEEE SSCI 2011 : Symposium Series on Computational Intelligence - T2FUZZ 2011: 2011 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems
Number of pages	6
Publication date	2011
Pages	88-93
Article number	5949558
ISBN (Print)	9781612840789
DOIs	https://doi.org/10.1109/T2FUZZ.2011.5949558
Publication status	Published - 2011
Externally published	Yes
Event	Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 1st Symposium on Advances in Type-2 Fuzzy Logic Systems, T2FUZZ 2011 - Paris, France Duration: 11 Apr 2011 → 15 Apr 2011

Conference

Conference	Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 1st Symposium on Advances in Type-2 Fuzzy Logic Systems, T2FUZZ 2011
Country	France
City	Paris
Period	11/04/2011 → 15/04/2011
Sponsor	IEEE

Keywords

Levenberg-Marquardt algorithm
Mackey-Glass time series
Type-2 fuzzy neural networks

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10.1109/T2FUZZ.2011.5949558

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Khanesar, M. A., Kayacan, E., Teshnehlab, M., & Kaynak, O. (2011). Levenberg Marquardt algorithm for the training of type-2 fuzzy neuro systems with a novel type-2 fuzzy membership function. In IEEE SSCI 2011: Symposium Series on Computational Intelligence - T2FUZZ 2011: 2011 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems (pp. 88-93). [5949558] https://doi.org/10.1109/T2FUZZ.2011.5949558

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title = "Levenberg Marquardt algorithm for the training of type-2 fuzzy neuro systems with a novel type-2 fuzzy membership function",

abstract = "A new training approach based on the Levenberg-Marquardt algorithm is proposed for type-2 fuzzy neural networks. While conventional gradient descent algorithms use only the first order derivative, the proposed algorithm used in this paper benefits from the first and the second order derivatives which makes the training procedure faster. Besides, this approach is more robust than the other techniques that use the second order derivatives, e.g. Gauss-Newton's method. The training algorithm proposed is tested on the training of a type-2 fuzzy neural network used for the prediction of a chaotic Mackey-Glass time series. The results show that the learning algorithm proposed not only results in faster training but also in a better forecasting accuracy.",

keywords = "Levenberg-Marquardt algorithm, Mackey-Glass time series, Type-2 fuzzy neural networks",

author = "Khanesar, {Mojtaba Ahmadieh} and Erdal Kayacan and Mohammad Teshnehlab and Okyay Kaynak",

year = "2011",

doi = "10.1109/T2FUZZ.2011.5949558",

language = "English",

isbn = "9781612840789",

pages = "88--93",

booktitle = "IEEE SSCI 2011",

note = "Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 1st Symposium on Advances in Type-2 Fuzzy Logic Systems, T2FUZZ 2011 ; Conference date: 11-04-2011 Through 15-04-2011",

}

Khanesar, MA, Kayacan, E, Teshnehlab, M & Kaynak, O 2011, Levenberg Marquardt algorithm for the training of type-2 fuzzy neuro systems with a novel type-2 fuzzy membership function. in IEEE SSCI 2011: Symposium Series on Computational Intelligence - T2FUZZ 2011: 2011 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems., 5949558, pp. 88-93, Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 1st Symposium on Advances in Type-2 Fuzzy Logic Systems, T2FUZZ 2011, Paris, France, 11/04/2011. https://doi.org/10.1109/T2FUZZ.2011.5949558

Levenberg Marquardt algorithm for the training of type-2 fuzzy neuro systems with a novel type-2 fuzzy membership function. / Khanesar, Mojtaba Ahmadieh; Kayacan, Erdal; Teshnehlab, Mohammad; Kaynak, Okyay.

IEEE SSCI 2011: Symposium Series on Computational Intelligence - T2FUZZ 2011: 2011 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems. 2011. p. 88-93 5949558.

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

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AU - Khanesar, Mojtaba Ahmadieh

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AU - Teshnehlab, Mohammad

AU - Kaynak, Okyay

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N2 - A new training approach based on the Levenberg-Marquardt algorithm is proposed for type-2 fuzzy neural networks. While conventional gradient descent algorithms use only the first order derivative, the proposed algorithm used in this paper benefits from the first and the second order derivatives which makes the training procedure faster. Besides, this approach is more robust than the other techniques that use the second order derivatives, e.g. Gauss-Newton's method. The training algorithm proposed is tested on the training of a type-2 fuzzy neural network used for the prediction of a chaotic Mackey-Glass time series. The results show that the learning algorithm proposed not only results in faster training but also in a better forecasting accuracy.

AB - A new training approach based on the Levenberg-Marquardt algorithm is proposed for type-2 fuzzy neural networks. While conventional gradient descent algorithms use only the first order derivative, the proposed algorithm used in this paper benefits from the first and the second order derivatives which makes the training procedure faster. Besides, this approach is more robust than the other techniques that use the second order derivatives, e.g. Gauss-Newton's method. The training algorithm proposed is tested on the training of a type-2 fuzzy neural network used for the prediction of a chaotic Mackey-Glass time series. The results show that the learning algorithm proposed not only results in faster training but also in a better forecasting accuracy.

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Khanesar MA, Kayacan E, Teshnehlab M, Kaynak O. Levenberg Marquardt algorithm for the training of type-2 fuzzy neuro systems with a novel type-2 fuzzy membership function. In IEEE SSCI 2011: Symposium Series on Computational Intelligence - T2FUZZ 2011: 2011 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems. 2011. p. 88-93. 5949558 https://doi.org/10.1109/T2FUZZ.2011.5949558

Levenberg Marquardt algorithm for the training of type-2 fuzzy neuro systems with a novel type-2 fuzzy membership function

Abstract

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Keywords

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