On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

Tohid Alizadeh, Karim Salahshoor, Mohammad Reza Jafari, Abdullah Alizadeh, Mehdi Gholami

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system.

Original languageEnglish
Title of host publication12th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2007 Proceedings
Pages257-264
Number of pages8
DOIs
Publication statusPublished - Dec 1 2007
Event12th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2007 - Patras, Greece
Duration: Sep 25 2007Sep 28 2007

Publication series

NameIEEE International Conference on Emerging Technologies and Factory Automation, ETFA

Other

Other12th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2007
CountryGreece
CityPatras
Period9/25/079/28/07

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Alizadeh, T., Salahshoor, K., Jafari, M. R., Alizadeh, A., & Gholami, M. (2007). On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network. In 12th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2007 Proceedings (pp. 257-264). [4416777] (IEEE International Conference on Emerging Technologies and Factory Automation, ETFA). https://doi.org/10.1109/EFTA.2007.4416777