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

被引：0
|
作者：
Alizadeh, Tohid [1 ]
Salahshoor, Karim [1 ]
Jafari, Mohammad Reza [1 ]
Alizadeh, Abdollah [2 ]
Gholami, Mehdi [1 ]
机构：
[1] Petr Univ Technol, Automat & Instrumentat Dept, PO Box 1455845168, Tehran, Iran
[2] Univ Tabriz, Bonab Fac Engn, Tabriz, Iran
来源：
ETFA 2007: 12TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION, VOLS 1-3 | 2007年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
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页码：257 / +
页数：2
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