Palmer, S;
Gorse, D;
Muk-Pavic, E;
(2015)
Neural Networks and Particle Swarm Optimization for Function Approximation in Tri-SWACH Hull Design.
In:
Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS).
ACM: New York.
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Abstract
Tri-SWACH is a novel multihull ship design that is well suited to a wide range of industrial, commercial, and military applications, but which because of its novelty has few experimental studies on which to base further development work. Using a new form of particle swarm optimization that incorporates a strong element of stochastic search, Breeding PSO, it is shown it is possible to use multilayer nets to predict resistance functions for Tri-SWACH hullforms, including one function, the Residual Resistance Coefficient, which was found intractable with previously explored neural network training methods.
| Type: | Proceedings paper |
|---|---|
| Title: | Neural Networks and Particle Swarm Optimization for Function Approximation in Tri-SWACH Hull Design |
| Event: | EANN '15 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/2797143.2797168 |
| Publisher version: | http://dx.doi.org/10.1145/2797143.2797168 |
| Language: | English |
| Additional information: | © ACM 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS), http://dx.doi.org/10.1145/10.1145/2797143.2797168 |
| Keywords: | Particle swarm optimization, function approximation, hullform design, multihull resistance, Tri-SWACH |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1469569 |
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