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dc.contributor.authorNeocleous, Constantinos C.en
dc.contributor.authorSchizas, Christos N.en
dc.creatorNeocleous, Constantinos C.en
dc.creatorSchizas, Christos N.en
dc.description.abstractThe proper selection of marine propellers is in general an involved and time-consuming design task. It is mostly based on searching large mounts of experimental data in order to find a proper matching of performance requirements and dimensional constraints. The present work has been done in order to improve the task of propeller design using techniques from the field of computational intelligence. An important requirement of the task was to help a designer to reach an acceptably good design in a fast and simple manner in which the most readily available propeller data are used as raw inputs. A neural network system has been developed that can help a naval architectural designer to select a suitable marine propeller that satisfies desired propulsion requirements. Different neural network architectures and learning parameters were tested, aiming at establishing a near optimum setup. To achieve this, a large number of experimental data was used. The end result in the network output is a set of suitable dimensional characteristics and a desired performance.en
dc.sourceProceedings of the International Joint Conference on Neural Networksen
dc.sourceInternational Joint Conference on Neural Networks (IJCNN'99)en
dc.subjectNeural networksen
dc.subjectData reductionen
dc.subjectComputational complexityen
dc.subjectConstraint theoryen
dc.subjectMachine designen
dc.subjectLearning systemsen
dc.subjectMarine propeller designen
dc.subjectNaval architectureen
dc.subjectShip propellersen
dc.titleMarine propeller design using artificial neural networksen
dc.description.endingpage3961 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.description.notes<p>Cited By :1</p>en
dc.contributor.orcidSchizas, Christos N. [0000-0001-6548-4980]

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