Design of a robust neural network structure for determining initial stability particulars of fishing vessels

Alkan, Ahmet; Gülez, Kayhan; Yilmaz, Hüseyin

doi:10.1016/j.oceaneng.2003.08.002

Cited by 23 publications

(11 citation statements)

References 4 publications

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“…The stability of a ship is of major importance and for this reason it was one of the first design areas in which computer tools were applied in order to facilitate the work of a naval architect. An illustrative example of stability calculations with the aid of neural networks is presented by Alkan et al (2004). They describe two models for the estimation of initial stability.…”

Section: Stabilitymentioning

confidence: 99%

The Utilization of Artificial Neural Networks in Marine Applications: An Overview

Gougoulidis¹

2008

Naval Engineers Journal

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Traditional calculation methods used in naval architecture and marine engineering rely mainly on statistical approaches and regression analysis. A relatively new tool in this field is the use of artificial neural networks. Artificial neural networks possess a number of unique characteristics that make them particularly attractive in complex problems, such as the ship design process. In this paper, an overview of the application of neural networks to various aspects of the ship design process is examined. Representative examples in the literature are presented in order to illustrate the power and the versatility of such an approach. The paper aims to provide the reader, whether a novice or an expert in the field of artificial intelligence and naval architecture, with useful sources as well as outline the potential for the extensive use of neural networks in naval applications.

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Section: Stabilitymentioning

confidence: 99%

The Utilization of Artificial Neural Networks in Marine Applications: An Overview

Gougoulidis¹

2008

Naval Engineers Journal

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“…As cited earlier as powerful computational tools, NN techniques have been used in many disciplines as well as in marine design fields (Raya et al 1996, Burns and Richter 1996, Haddara and Jinsong 1998, Reicha and Barai 2000, Deo and Jagdale 2003, and Alkan et al, 2004. Haykin stated a neural network made up of non-linear neurons, which has a natural ability to realize (approximate) non-linear input-output (Haykin 1999).…”

Section: Neural Network Structurementioning

confidence: 99%

“…In this paper a robust tool is designed with the help of a NN structure that uses both the classical back-propagation (CBA) and the fast back-propagation algorithms (FBA). It would be worthy to refer to a more recent study performed by the authors of the present paper (Alkan et al 2004) where the NN technique was used to determine preliminary stability particulars of fishing vessels having a large range of hull geometric parameters, where both the CBA and the FBA algorithms were used. The study offers a robust NN structure enabling the designer at the preliminary design stage to obtain vessel stability particulars and gives an almost exact idea for determining design parameters affecting stability.…”

Section: Introductionmentioning

confidence: 99%

A Knowledge‐Based Computational Design Tool for Determining Preliminary Stability Particulars of Naval Ships

Alkan

Gülez

2004

Naval Engineers Journal

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The preliminary design stage of a ship constitutes an important base from which to develop the preliminary ship characteristics to be used as goals in the following stages of the ship design process. Thus, reliable and efficient design tools are required by ship designers to determine ship design particulars that can satisfy various performance measures of stability for safety requirements, as well as resistance, volume, and load capacity to set economic targets. In this study, a robust neural network (NN) structure is established and using principle design data from 22 naval ships (Bartholomew et al. 1992), a reliable design tool for ship designers for determining ship preliminary stability particulars and as well as load capacity is developed. In the NN structure the classical back‐propagation (CBA) (Rumelhart et al. 1986) and fast back‐propagation (FBA) (Karayiannis and Venetsanopoulas 1991, 1992, 1993) algorithms are employed. The user inputs a set of common ship hull form parameters, while the output from the NN consists of the transverse center of gravity above keel (KG), the transverse metacenter above keel (KM), and the transverse metacenter above center of buoyancy (BM). Further, the dead‐weight tonnage capacity (DWT) is also determined using the same NN structure. According to various tests of the NN structure it is demonstrated that the model is able to give highly sensitive outputs for naval vessels even using a more restricted input data set. Depending on the availability of the design data structure the proposed model can be reconstructed for other preliminary design objectives such as predicting the powering performance for different classes of ships.

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“…The loading capacity of the vessel is the input to the network which estimates six parameters, namely, length, breadth, speed, draft, depth and displacement. Alkan et al (2004) propose two ANNs for determining initial stability particulars of fishing vessels. The architecture is MLP with two hidden layers.…”

Section: Introductionmentioning

confidence: 99%

A flexible system for initial ship design parameters estimation using a system of neural networks

Senousy¹,

Abou-Elmakarem²

2011

J. nav. arch. mar. engg.

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Design of a robust neural network structure for determining initial stability particulars of fishing vessels

Cited by 23 publications

References 4 publications

The Utilization of Artificial Neural Networks in Marine Applications: An Overview

The Utilization of Artificial Neural Networks in Marine Applications: An Overview

A Knowledge‐Based Computational Design Tool for Determining Preliminary Stability Particulars of Naval Ships

A flexible system for initial ship design parameters estimation using a system of neural networks

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