The Improving of Neural Network Capabilities in On-Line Identification and Tracking Control of Ship (CROSBI ID 500278)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Velagić, Jasmin ; Vukić, Zoran
engleski
The Improving of Neural Network Capabilities in On-Line Identification and Tracking Control of Ship
The paper proposes a computationally efficient artificial neural network model for on-line system identification of nonlinear systems under the fuzzy closed-loop control system. The proposed backprogagation (BP) algorithm with adaptive learning rate (BPLAR) was tested for both off-line and on-line identification, comparing with traditional backpropagation learning algorithm on nonlinear ship model. The disadvantages of conventional BP algorithm are slower convergence and longer training times. The learning rate is adjusted at each iteration for the on-line weight and bias adaptation to reduce the training time. Simulation results indicate a superior convergence speed and better control performance in the case of adaptive BP method
Neural networks; adaptive learning rate; identification; tracking control
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Podaci o prilogu
195-200-x.
2004.
objavljeno
Podaci o matičnoj publikaciji
Szczecin: Wydawnictwo Uczelniane Politechniki Szczecinskiej
83-88764-09-8
Podaci o skupu
10th IEEE International Conference on Methods and Models in Automation and Robotics(MMAR) - Special Invited Session "Advanced Ship Control Systems"
predavanje
30.08.2004-02.09.2004
Międzyzdroje, Poljska