Modeling of ship vertical motion with self-organizing radial basis function artificial neural network

• Published in: 2006 1st International Symposium on Systems and Control in Aerospace and Astronautics pp. 6 pp. - 1136
• Main Authors: Xuejing Yang, Xiren Zhao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2006
• Subjects: Artificial intelligence; Artificial neural networks; Automatic control; Deductive databases; Intelligent networks; Marine vehicles; Motion control; Navigation; Ocean waves; Radial basis function networks
• ISBN: 0780393953; 9780780393950
• DOI: 10.1109/ISSCAA.2006.1627566

Ships' vertical motion caused by random disturbances of ocean wave is unsafe for navigation and carrier planes' takeoff and landing. To reduce the vertical motion and give an effective control for ship's motion pose, an intelligent model of ship's vertical motion is needed. With the experimental data, based on the self-organizing radial basis function neural network, an intelligent model of vertical motion which can self-adapt with navigating speed, navigating course and ocean condition is presented. The automatic configuration and learning of the network are carried out by using a self-organizing learning algorithm. The results of simulation indicate that the performance of self-organizing radial basis function neural network is better than that of the radial basis function neural network without self-organizing learning