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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)
Tohid Alizadeh ; Automation and Instrumentation Department, Petroleum University of Technology, Tehran, Iran ; Karim Salahshoor ; Mohammad Reza Jafari ; Abdollah Alizadeh
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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:

2007 IEEE Conference on Emerging Technologies and Factory Automation (EFTA 2007)

Date of Conference:

25-28 Sept. 2007