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

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5 Author(s)
Alizadeh, T. ; Petroleum Univ. of Technol., Tehran ; Salahshoor, K. ; Jafari, M.R. ; Alizadeh, A.
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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.

Published in:

Emerging Technologies and Factory Automation, 2007. ETFA. IEEE Conference on

Date of Conference:

25-28 Sept. 2007