A comparison of PSO and backpropagation combined with LS and RLS in identification using fuzzy neural networks

Niuslia Shafiabady; M. Teshnehlab; M. Aliyari Shooredeh

doi:10.1109/ICIT.2006.372464

A comparison of PSO and backpropagation combined with LS and RLS in identification using fuzzy neural networks

Niuslia Shafiabady, M. Teshnehlab, M. Aliyari Shooredeh

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

Abstract

In this article using a population-based method, Particle Swarm Optimization in training the standard deviation and centers of radial basis function fuzzy neural networks is put into practice and the results are compared with training the same networks' standard deviation and centers using backpropagation. We have applied Least Square and Recursive Least Square in training the weights of this fuzzy neural networks . There are four sets of data used to examine and prove that according to the convergence speed and the identification error particle swarm optimization works better and as its complexity is much less, it can be suggested as a good solution for training the parameters.

Original language	English
Title of host publication	2006 IEEE International Conference on Industrial Technology, ICIT
Pages	1574-1579
Number of pages	6
DOIs	https://doi.org/10.1109/ICIT.2006.372464
Publication status	Published - 2006
Externally published	Yes
Event	2006 IEEE International Conference on Industrial Technology, ICIT - Mumbai, India Duration: 15 Dec 2006 → 17 Dec 2006

Publication series

Name	Proceedings of the IEEE International Conference on Industrial Technology

Conference

Conference	2006 IEEE International Conference on Industrial Technology, ICIT
Country/Territory	India
City	Mumbai
Period	15/12/06 → 17/12/06

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10.1109/ICIT.2006.372464

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Shafiabady, N., Teshnehlab, M., & Aliyari Shooredeh, M. (2006). A comparison of PSO and backpropagation combined with LS and RLS in identification using fuzzy neural networks. In 2006 IEEE International Conference on Industrial Technology, ICIT (pp. 1574-1579). [4237786] (Proceedings of the IEEE International Conference on Industrial Technology). https://doi.org/10.1109/ICIT.2006.372464

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title = "A comparison of PSO and backpropagation combined with LS and RLS in identification using fuzzy neural networks",

abstract = "In this article using a population-based method, Particle Swarm Optimization in training the standard deviation and centers of radial basis function fuzzy neural networks is put into practice and the results are compared with training the same networks' standard deviation and centers using backpropagation. We have applied Least Square and Recursive Least Square in training the weights of this fuzzy neural networks . There are four sets of data used to examine and prove that according to the convergence speed and the identification error particle swarm optimization works better and as its complexity is much less, it can be suggested as a good solution for training the parameters.",

keywords = "FNN, GD, Identification, LS, PSO, RBF, RLS",

author = "Niuslia Shafiabady and M. Teshnehlab and {Aliyari Shooredeh}, M.",

year = "2006",

doi = "10.1109/ICIT.2006.372464",

language = "English",

isbn = "1424407265",

series = "Proceedings of the IEEE International Conference on Industrial Technology",

pages = "1574--1579",

booktitle = "2006 IEEE International Conference on Industrial Technology, ICIT",

note = "2006 IEEE International Conference on Industrial Technology, ICIT ; Conference date: 15-12-2006 Through 17-12-2006",

}

Shafiabady, N, Teshnehlab, M & Aliyari Shooredeh, M 2006, A comparison of PSO and backpropagation combined with LS and RLS in identification using fuzzy neural networks. in 2006 IEEE International Conference on Industrial Technology, ICIT., 4237786, Proceedings of the IEEE International Conference on Industrial Technology, pp. 1574-1579, 2006 IEEE International Conference on Industrial Technology, ICIT, Mumbai, India, 15/12/06. https://doi.org/10.1109/ICIT.2006.372464

A comparison of PSO and backpropagation combined with LS and RLS in identification using fuzzy neural networks. / Shafiabady, Niuslia; Teshnehlab, M.; Aliyari Shooredeh, M.
2006 IEEE International Conference on Industrial Technology, ICIT. 2006. p. 1574-1579 4237786 (Proceedings of the IEEE International Conference on Industrial Technology).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

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T1 - A comparison of PSO and backpropagation combined with LS and RLS in identification using fuzzy neural networks

AU - Shafiabady, Niuslia

AU - Teshnehlab, M.

AU - Aliyari Shooredeh, M.

PY - 2006

Y1 - 2006

N2 - In this article using a population-based method, Particle Swarm Optimization in training the standard deviation and centers of radial basis function fuzzy neural networks is put into practice and the results are compared with training the same networks' standard deviation and centers using backpropagation. We have applied Least Square and Recursive Least Square in training the weights of this fuzzy neural networks . There are four sets of data used to examine and prove that according to the convergence speed and the identification error particle swarm optimization works better and as its complexity is much less, it can be suggested as a good solution for training the parameters.

AB - In this article using a population-based method, Particle Swarm Optimization in training the standard deviation and centers of radial basis function fuzzy neural networks is put into practice and the results are compared with training the same networks' standard deviation and centers using backpropagation. We have applied Least Square and Recursive Least Square in training the weights of this fuzzy neural networks . There are four sets of data used to examine and prove that according to the convergence speed and the identification error particle swarm optimization works better and as its complexity is much less, it can be suggested as a good solution for training the parameters.

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KW - GD

KW - Identification

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KW - PSO

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M3 - Conference Paper published in Proceedings

SN - 1424407265

SN - 9781424407262

T3 - Proceedings of the IEEE International Conference on Industrial Technology

SP - 1574

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BT - 2006 IEEE International Conference on Industrial Technology, ICIT

T2 - 2006 IEEE International Conference on Industrial Technology, ICIT

Y2 - 15 December 2006 through 17 December 2006

ER -

A comparison of PSO and backpropagation combined with LS and RLS in identification using fuzzy neural networks

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