Prediction of the Spatial Orientation of a Ship by Means of Neural Networks

Tomasz Praczyk; Piotr Szymak

doi:10.4028/www.scientific.net/SSP.210.223

Paper Titles

Hybrid Position/Force Control in Robotised Machining
p.192

Laboratory Control and Measurement System of a Dual-Fuel Compression Ignition Combustion Engine Operating in a Cogeneration System
p.200

Neuro-Dynamic Programming in Control of the Ball and Beam System
p.206

Optimization-Supported Decision-Making in the Marine Game Environment
p.215

Prediction of the Spatial Orientation of a Ship by Means of Neural Networks
p.223

Safe Ship Control Method with the Use of Ant Colony Optimization
p.234

Selected Problems of the Asynchronous Drive Control with the Three-Phase Soft-Start System
p.245

System Architecture of Mission Planning and Autonomous Surface Vessel Control
p.252

The Characteristics of the Matrix Game Sensitivity of the Safe Passing of Ships at Sea
p.258

HomeSolid State PhenomenaSolid State Phenomena Vol. 210Prediction of the Spatial Orientation of a Ship by...

Prediction of the Spatial Orientation of a Ship by Means of Neural Networks

Abstract:

When designing a control system for a ship gun, the problem is to predict orientation of the ship in an assumed reference frame. This problem can be solved with different regression tools out of which one are neural networks. To verify their abilities to predict spatial orientation of a ship, experiments were carried out. In the experiments, the task of neural networks was to predict the roll angle which changed according to seven model sinusoid functions with different parameters and higher harmonic components.

You might also be interested in these eBooks

Mechatronic Systems, Mechanics and Materials II

View Preview

Info:

Periodical:

Solid State Phenomena (Volume 210)

Pages:

223-233

DOI:

https://doi.org/10.4028/www.scientific.net/SSP.210.223

Citation:

Cite this paper

Online since:

October 2013

Authors:

Tomasz Praczyk, Piotr Szymak

Keywords:

Automatic Ship Gun, Prediction, Regression Methods

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Floreano D. and Urzelai J., Evolutionary robots with online self-organization and behavioral fitness, Neural Networks, Vol. 13, pp.431-443, (2000).

DOI: 10.1016/s0893-6080(00)00032-0

Google Scholar

[2] Miller G.F., Todd P.M., Hegde S.U., Designing Neural Networks Using Genetic Algorithms. Proceedings of the Third International Conference on Genetic Algorithms, pp.379-384, (1989).

Google Scholar

[3] Potter M., The Design and Analysis of a Computational Model of Cooperative Coevolution. PhD thesis, George Mason University, Fairfax, Virginia (1997).

Google Scholar

[4] Potter M. A., De Jong K. A., A Cooperative Coevolutionary Approach to Function Optimization. The Third Parallel Problem Solving From Nature, Jerusalem, Israel, pp.249-257, (1994).

DOI: 10.1007/3-540-58484-6_269

Google Scholar

[5] Praczyk T. Assembler Encoding with Evolvable Operations – in review.

Google Scholar

[6] Praczyk T., Forming Dynamic, Self-organizing Neural Networks by means of Assembler Encoding, Biuletyn WAT, Vol. LX, No. 2, 2011, pp.117-138.

Google Scholar

[7] Praczyk T., Szymak P. Prediction of spatial orientation of 35mm gun ship, Conference P roceedings of 5th SAUMO, Dęblin (2013).

Google Scholar

[8] Specht D. F., A general regression neural network., IEEE Transactions on Neural Networks, vol. 2 no. 7, (1991).

Google Scholar