Neural network applications in naval architecture and marine engineering

Ray, Tapabrata; Gokarn, R. P.; Sha, Om Prakash

doi:10.1016/0954-1810(95)00030-5

Cited by 23 publications

(5 citation statements)

References 7 publications

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“…NNs are successfully used in areas such as control (Miller et al, 1995), early detection of machine faults as well as design problems including marine vehicles (Raya et al, 1996), the identification of ship coupled heave-pitch motions (Haddara and Jinsong, 1998) and the detection of the extent and location of ship damage (Zubaydi et al, 2002). The feed-forward NN is usually trained by a backpropagation training algorithm first proposed by Rumelhart, Hinton, and Williams in 1986.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

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

2004

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Section: Neural Network (Nn)mentioning

confidence: 99%

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

2004

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“…The general arrangements of a ship mainly depend on the role of the ship and the transported cargo. An example of determining indirectly the general arrangements of a containership is presented by Ray et al (1996), where the number of containers carried by a ship is predicted. In their study, a feedforward neural network with supervised learning is created.…”

Section: General Arrangementsmentioning

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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“…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%

“…The basic problem is because of the weakness of the classical fitting techniques in representing the non-linearity in the physical model. With the benefit of the efficient algorithms presented by neural networks (NN), the identification of high precision non-linear relations becomes easier (Raya et al 1996, Burns and Richter 1996, Haddara and Jinsong 1998, Reicha and Barai 2000, Deo and Jagdale 2003, Alkan et al 2003. Raya et al discussed the usefulness of NN back propagation algorithm for marine design problems and made two applications for predicting the container capacity of ships and the added mass coefficients for asymmetric bodies in motion.…”

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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Neural network applications in naval architecture and marine engineering

Cited by 23 publications

References 7 publications

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

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

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

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