14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract

Close

Paper WeA6.3

Van de Ven, Pepijn W.J. (Univ. of Limerick), Refsnes, Jon (Norwegian Univ. of Science and Tech.), Johansen, Tor A. (Norwegian Univ. of Science and Tech.), Flanagan, Colin (Univ. of Limerick), Toal, Daniel (Univ. of Limerick)

IDENTIFICATION OF MINESNIPERíS DAMPING PARAMETERS USING NEURAL NETWORKS Experimental Results

Scheduled for presentation during the Invited Session "Essential Aspects of Marine Systems Dynamics Identification" (WeA6), Wednesday, March 29, 2006, 11:10−11:30, Newcastle Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 17, 2018

Keywords Neural Networks, Nonlinear System Identification, Identification for Control

Abstract

In this article experimental work is presented on the identification of the damping parameters in a new defence system, called Minesniper. Simulations have revealed that the standard identification model for the damping parameters with a linear and a quadratic term yields inaccurate results. Therefore, neural networks are used to represent the damping. As the available, noisy training data set is too short for a full identification of the dynamics, feedback neural networks encounter problems in representing the damping in regions that have not been visited during training. To alleviate this problem radial basis functions have been applied successfully.