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 publicationIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Pages257-264
Number of pages8
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event12th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2007 - Patras, Greece
Duration: Sep 25 2007Sep 28 2007

Other

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

Fingerprint

Hybrid systems
Neural networks
Kalman filters
Learning algorithms

ASJC Scopus subject areas

  • Engineering(all)

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 IEEE International Conference on Emerging Technologies and Factory Automation, ETFA (pp. 257-264). [4416777] https://doi.org/10.1109/EFTA.2007.4416777

On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network. / Alizadeh, Tohid; Salahshoor, Karim; Jafari, Mohammad Reza; Alizadeh, Abdullah; Gholami, Mehdi.

IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. 2007. p. 257-264 4416777.

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

Alizadeh, T, Salahshoor, K, Jafari, MR, Alizadeh, A & Gholami, M 2007, On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network. in IEEE International Conference on Emerging Technologies and Factory Automation, ETFA., 4416777, pp. 257-264, 12th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2007, Patras, Greece, 9/25/07. https://doi.org/10.1109/EFTA.2007.4416777
Alizadeh T, Salahshoor K, Jafari MR, Alizadeh A, Gholami M. On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network. In IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. 2007. p. 257-264. 4416777 https://doi.org/10.1109/EFTA.2007.4416777
Alizadeh, Tohid ; Salahshoor, Karim ; Jafari, Mohammad Reza ; Alizadeh, Abdullah ; Gholami, Mehdi. / On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network. IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. 2007. pp. 257-264
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