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