State-space fuzzy-neural network for modeling of nonlinear dynamics

Yancho Todorov; Margarita Terziyska

doi:10.1109/INISTA.2014.6873620

State-space fuzzy-neural network for modeling of nonlinear dynamics

Yancho Todorov, Margarita Terziyska

Not published at VTT

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

3 Citations (Scopus)

Abstract

This paper describes a novel idea for designing a fuzzy-neural network for modeling of nonlinear system dynamics. The presented approach assumes a state-space representation in order to obtain a more compact form of the model, without statement of a great number of parameters needed to represent a nonlinear behavior. To increase the flexibility of the network, simple Takagi-Sugeno inferences are used to estimate the current system states, by a set of a multiple local linear state estimators. Afterwards, the output of the network is defined, as function of the current and estimated system parameters. A simple learning algorithm based on two step Gradient descent procedure to adjust the network parameters, is applied. The potentials of the proposed modeling network are demonstrated by simulation experiments to model an oscillating pendulum and a nonlinear drying plant.

Original language	English
Title of host publication	2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	212-217
ISBN (Electronic)	978-1-4799-3020-3
ISBN (Print)	978-1-4799-3019-7
DOIs	https://doi.org/10.1109/INISTA.2014.6873620
Publication status	Published - 1 Jan 2014
MoE publication type	A4 Article in a conference publication
Event	2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications, INISTA 2014 - Alberobello, Italy Duration: 23 Jun 2014 → 25 Jun 2014

Conference

Conference	2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications, INISTA 2014
Country/Territory	Italy
City	Alberobello
Period	23/06/14 → 25/06/14

Keywords

fuzzy-neural systems
Gradient descent
State-space
Takagi-Sugeno

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10.1109/INISTA.2014.6873620

Cite this

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title = "State-space fuzzy-neural network for modeling of nonlinear dynamics",

abstract = "This paper describes a novel idea for designing a fuzzy-neural network for modeling of nonlinear system dynamics. The presented approach assumes a state-space representation in order to obtain a more compact form of the model, without statement of a great number of parameters needed to represent a nonlinear behavior. To increase the flexibility of the network, simple Takagi-Sugeno inferences are used to estimate the current system states, by a set of a multiple local linear state estimators. Afterwards, the output of the network is defined, as function of the current and estimated system parameters. A simple learning algorithm based on two step Gradient descent procedure to adjust the network parameters, is applied. The potentials of the proposed modeling network are demonstrated by simulation experiments to model an oscillating pendulum and a nonlinear drying plant.",

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note = "2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications, INISTA 2014 ; Conference date: 23-06-2014 Through 25-06-2014",

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Todorov, Y & Terziyska, M 2014, State-space fuzzy-neural network for modeling of nonlinear dynamics. in 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)., 6873620, IEEE Institute of Electrical and Electronic Engineers, pp. 212-217, 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications, INISTA 2014, Alberobello, Italy, 23/06/14. https://doi.org/10.1109/INISTA.2014.6873620

State-space fuzzy-neural network for modeling of nonlinear dynamics. / Todorov, Yancho; Terziyska, Margarita.
2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA). IEEE Institute of Electrical and Electronic Engineers, 2014. p. 212-217 6873620.

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

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AB - This paper describes a novel idea for designing a fuzzy-neural network for modeling of nonlinear system dynamics. The presented approach assumes a state-space representation in order to obtain a more compact form of the model, without statement of a great number of parameters needed to represent a nonlinear behavior. To increase the flexibility of the network, simple Takagi-Sugeno inferences are used to estimate the current system states, by a set of a multiple local linear state estimators. Afterwards, the output of the network is defined, as function of the current and estimated system parameters. A simple learning algorithm based on two step Gradient descent procedure to adjust the network parameters, is applied. The potentials of the proposed modeling network are demonstrated by simulation experiments to model an oscillating pendulum and a nonlinear drying plant.

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State-space fuzzy-neural network for modeling of nonlinear dynamics

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Keywords

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