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

Yancho Todorov, Margarita Terziyska

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

2 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 languageEnglish
Title of host publication2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages212-217
ISBN (Electronic)978-1-4799-3020-3
ISBN (Print)978-1-4799-3019-7
DOIs
Publication statusPublished - 1 Jan 2014
MoE publication typeA4 Article in a conference publication
Event2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications, INISTA 2014 - Alberobello, Italy
Duration: 23 Jun 201425 Jun 2014

Conference

Conference2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications, INISTA 2014
CountryItaly
CityAlberobello
Period23/06/1425/06/14

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Keywords

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

Cite this

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) (pp. 212-217). [6873620] IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/INISTA.2014.6873620