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Journal of Applied Mathematics
Volume 2014 (2014), Article ID 759834, 11 pages
http://dx.doi.org/10.1155/2014/759834
Research Article

Comparison of Neural Network Error Measures for Simulation of Slender Marine Structures

1DNV Denmark A/S, Tuborg Parkvej 8, 2900 Hellerup, Denmark
2Department of Mechanical Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
3Det Norske Veritas, 7496 Trondheim, Norway
4Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark

Received 19 November 2013; Accepted 21 January 2014; Published 2 March 2014

Academic Editor: Ricardo Perera

Copyright © 2014 Niels H. Christiansen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Training of an artificial neural network (ANN) adjusts the internal weights of the network in order to minimize a predefined error measure. This error measure is given by an error function. Several different error functions are suggested in the literature. However, the far most common measure for regression is the mean square error. This paper looks into the possibility of improving the performance of neural networks by selecting or defining error functions that are tailor-made for a specific objective. A neural network trained to simulate tension forces in an anchor chain on a floating offshore platform is designed and tested. The purpose of setting up the network is to reduce calculation time in a fatigue life analysis. Therefore, the networks trained on different error functions are compared with respect to accuracy of rain flow counts of stress cycles over a number of time series simulations. It is shown that adjusting the error function to perform significantly better on a specific problem is possible. On the other hand. it is also shown that weighted error functions actually can impair the performance of an ANN.