Recurrent fuzzy-neural network with fast learning algorithm for predictive control

Yancho Todorov; Margarita Terzyiska; Michail Petrov

doi:10.1007/978-3-642-40728-4_58

Recurrent fuzzy-neural network with fast learning algorithm for predictive control

Yancho Todorov
, Margarita Terzyiska
, Michail Petrov

Not published at Aalto University

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

7 Citations (Scopus)

Abstract

This paper presents a Takagi-Sugeno type recurrent fuzzy-neural network with a global feedback. To improve the predictions and to minimize the possible model oscillations, a hybrid learning procedure based on Gradient descent and the fast converging Gauss-Newton algorithms, is designed. The model performance is evaluated in prediction of two chaotic time series - Mackey-Glass and Rossler. The proposed recurrent fuzzy-neural network is coupled with analytical optimization approach in a Model Predictive Control scheme. The potentials of the obtained predictive controller are demonstrated by simulation experiments to control a nonlinear Continuous Stirred Tank Reactor.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings
Publisher	Springer
Pages	459-466
Number of pages	8
ISBN (Print)	9783642407277
DOIs	https://doi.org/10.1007/978-3-642-40728-4_58
Publication status	Published - 2013
MoE publication type	A4 Conference publication
Event	International Conference on Artificial Neural Networks - Sofia, Bulgaria Duration: 10 Sept 2013 → 13 Sept 2013 Conference number: 23

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8131 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Artificial Neural Networks
Abbreviated title	ICANN
Country/Territory	Bulgaria
City	Sofia
Period	10/09/2013 → 13/09/2013

Keywords

Gauss-Newton method
Gradient descent
momentum learning
optimization
predictive control
recurrent fuzzy-neural networks
Takagi-Sugeno

Access to Document

10.1007/978-3-642-40728-4_58

Cite this

Todorov, Y., Terzyiska, M., & Petrov, M. (2013). Recurrent fuzzy-neural network with fast learning algorithm for predictive control. In Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings (pp. 459-466). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8131 LNCS). Springer. https://doi.org/10.1007/978-3-642-40728-4_58

Todorov, Yancho ; Terzyiska, Margarita ; Petrov, Michail. / Recurrent fuzzy-neural network with fast learning algorithm for predictive control. Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings. Springer, 2013. pp. 459-466 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Recurrent fuzzy-neural network with fast learning algorithm for predictive control",

abstract = "This paper presents a Takagi-Sugeno type recurrent fuzzy-neural network with a global feedback. To improve the predictions and to minimize the possible model oscillations, a hybrid learning procedure based on Gradient descent and the fast converging Gauss-Newton algorithms, is designed. The model performance is evaluated in prediction of two chaotic time series - Mackey-Glass and Rossler. The proposed recurrent fuzzy-neural network is coupled with analytical optimization approach in a Model Predictive Control scheme. The potentials of the obtained predictive controller are demonstrated by simulation experiments to control a nonlinear Continuous Stirred Tank Reactor.",

keywords = "Gauss-Newton method, Gradient descent, momentum learning, optimization, predictive control, recurrent fuzzy-neural networks, Takagi-Sugeno",

author = "Yancho Todorov and Margarita Terzyiska and Michail Petrov",

year = "2013",

doi = "10.1007/978-3-642-40728-4\_58",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings",

address = "Germany",

note = "International Conference on Artificial Neural Networks, ICANN ; Conference date: 10-09-2013 Through 13-09-2013",

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Todorov, Y, Terzyiska, M & Petrov, M 2013, Recurrent fuzzy-neural network with fast learning algorithm for predictive control. in Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8131 LNCS, Springer, pp. 459-466, International Conference on Artificial Neural Networks, Sofia, Bulgaria, 10/09/2013. https://doi.org/10.1007/978-3-642-40728-4_58

Recurrent fuzzy-neural network with fast learning algorithm for predictive control. / Todorov, Yancho; Terzyiska, Margarita; Petrov, Michail.
Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings. Springer, 2013. p. 459-466 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8131 LNCS).

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

TY - GEN

T1 - Recurrent fuzzy-neural network with fast learning algorithm for predictive control

AU - Todorov, Yancho

AU - Terzyiska, Margarita

AU - Petrov, Michail

N1 - Conference code: 23

PY - 2013

Y1 - 2013

N2 - This paper presents a Takagi-Sugeno type recurrent fuzzy-neural network with a global feedback. To improve the predictions and to minimize the possible model oscillations, a hybrid learning procedure based on Gradient descent and the fast converging Gauss-Newton algorithms, is designed. The model performance is evaluated in prediction of two chaotic time series - Mackey-Glass and Rossler. The proposed recurrent fuzzy-neural network is coupled with analytical optimization approach in a Model Predictive Control scheme. The potentials of the obtained predictive controller are demonstrated by simulation experiments to control a nonlinear Continuous Stirred Tank Reactor.

AB - This paper presents a Takagi-Sugeno type recurrent fuzzy-neural network with a global feedback. To improve the predictions and to minimize the possible model oscillations, a hybrid learning procedure based on Gradient descent and the fast converging Gauss-Newton algorithms, is designed. The model performance is evaluated in prediction of two chaotic time series - Mackey-Glass and Rossler. The proposed recurrent fuzzy-neural network is coupled with analytical optimization approach in a Model Predictive Control scheme. The potentials of the obtained predictive controller are demonstrated by simulation experiments to control a nonlinear Continuous Stirred Tank Reactor.

KW - Gauss-Newton method

KW - Gradient descent

KW - momentum learning

KW - optimization

KW - predictive control

KW - recurrent fuzzy-neural networks

KW - Takagi-Sugeno

UR - https://www.scopus.com/pages/publications/84884929578

U2 - 10.1007/978-3-642-40728-4_58

DO - 10.1007/978-3-642-40728-4_58

M3 - Conference article in proceedings

AN - SCOPUS:84884929578

SN - 9783642407277

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 459

EP - 466

BT - Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings

PB - Springer

T2 - International Conference on Artificial Neural Networks

Y2 - 10 September 2013 through 13 September 2013

ER -

Todorov Y, Terzyiska M, Petrov M. Recurrent fuzzy-neural network with fast learning algorithm for predictive control. In Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings. Springer. 2013. p. 459-466. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-40728-4_58

Recurrent fuzzy-neural network with fast learning algorithm for predictive control

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Keywords

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