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

2011 ◽  
Vol 8 (2) ◽  
pp. 71-82 ◽  
Author(s):  
Hamada Senousy ◽  
Mahmoud Abou-Elmakarem
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ship Design ◽  
Parameters Estimation ◽  
Design Parameters ◽  
Reliable Estimate ◽  
Multilayer Perceptrons ◽  
New Approach ◽  
Flexible System ◽  
Artificial Neural

To initialize ship design process, it is very important to be able to develop an initial estimate of ship parameters to satisfy designer required specifications. For new emerging designs, this estimate has to be made based on a limited available set of examples. Moreover, a practical estimate prediction strategy should be flexible enough having no distinction between input (specified constraints) and outputs (parameters required to be estimated), since these vary from one design case to another.  Conventional regression-based techniques, which are usually employed to provide the required estimates, suffer from low accuracy in case of a small number of available examples. In addition to that, they fail to capture the interrelation between different design parameters. To overcome these limitations and others, the present paper proposes a new approach based on a system of artificial neural-networks (ANNs). The new approach not only overcomes regression limitations but is also capable of providing a reliable estimate of initial design offset table based on different ANN outputs.  The paper uses a case study for demonstrating the merits of the proposed approach.Keywords: Ship design; regression; ship series; Artificial Neural Networks (ANNs); Multilayer Perceptrons (MLPs); Normalized Gaussian Modified Lagrangian (NGML) doi: http://dx.doi.org/10.3329/jname.v8i2.6945 Journal of Naval Architecture and Marine Engineering 8(2011) 71-82

scholarly journals Approximation of the index for assessing ship sea-keeping performance on the basis of ship design parameters

2007 ◽  
Vol 14 (3) ◽  
pp. 21-26 ◽  
Author(s):  
Tomasz Cepowski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ship Design ◽  
Design Parameters ◽  
Preliminary Design ◽  
Operational Effectiveness ◽  
Design Problems ◽  
New Approach ◽  
Analytical Functions ◽  
Effectiveness Index

Approximation of the index for assessing ship sea-keeping performance on the basis of ship design parameters This paper presents a new approach which makes it possible to take into account seakeeping qualities of ship in the preliminary stage of its design. The presented concept is based on representing ship's behaviour in waves by means of the so called operational effectiveness index. Presented values of the index were calculated for a broad range of design parameters. On this basis were elaborated analytical functions which approximate the index depending on ship design parameters. Also, example approximations of the index calculated by using artificial neural networks, are attached. The presented approach may find application to ship preliminary design problems as well as in ship service stage to assess sea-keeping performance of a ship before its departure to sea.

scholarly journals The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 721
Author(s):  
Krzysztof Adamczyk ◽  
Wilhelm Grzesiak ◽  
Daniel Zaborski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Multilayer Perceptrons ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Holstein Friesian ◽  
Artificial Neural ◽  
Friesian Cows

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

scholarly journals Estimation of electrical resistivity using artificial neural networks: a case study from Lublin Basin, SE Poland

2021 ◽  
Author(s):  
Jakub Ważny ◽  
Michał Stefaniuk ◽  
Adam Cygal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Training Data ◽  
Multilayer Perceptrons ◽  
Borehole Data ◽  
Geological Interpretation ◽  
Se Poland ◽  
Geological Conditions ◽  
Artificial Neural ◽  
Reliable Solution

AbstractArtificial neural networks method (ANNs) is a common estimation tool used for geophysical applications. Considering borehole data, when the need arises to supplement a missing well log interval or whole logging—ANNs provide a reliable solution. Supervised training of the network on a reliable set of borehole data values with further application of this network on unknown wells allows creation of synthetic values of missing geophysical parameters, e.g., resistivity. The main assumptions for boreholes are: representation of similar geological conditions and the use of similar techniques of well data collection. In the analyzed case, a set of Multilayer Perceptrons were trained on five separate chronostratigraphic intervals of borehole, considered as training data. The task was to predict missing deep laterolog (LLD) logging in a borehole representing the same sequence of layers within the Lublin Basin area. Correlation between well logs data exceeded 0.8. Subsequently, magnetotelluric parametric soundings were modeled and inverted on both boreholes. Analysis showed that congenial Occam 1D models had better fitting of TM mode of MT data in each case. Ipso facto, synthetic LLD log could be considered as a basis for geophysical and geological interpretation. ANNs provided solution for supplementing datasets based on this analytical approach.

scholarly journals Optimization of a thin-walled element geometry using a system integrating neural networks and finite element method

2017 ◽  
Vol 62 (1) ◽  
pp. 435-442 ◽  
Author(s):  
P. Golewski ◽  
J. Gajewski ◽  
T. Sadowski
Keyword(s):  
Neural Networks ◽  
Finite Element Method ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Integrated System ◽  
Multilayer Perceptrons ◽  
Optimization Of Geometry ◽  
Thin Walled ◽  
Artificial Neural ◽  
Element Method

Abstract Artificial neural networks [ANNs] are an effective method for predicting and classifying variables. This article presents the application of an integrated system based on artificial neural networks and calculations by the finite element method [FEM] for the optimization of geometry of a thin-walled element of an air structure. To ensure optimal structure, the structure’s geometry was modified by creating side holes and ribs, also with holes. The main criterion of optimization was to reduce the structure’s weight at the lowest possible deformation of the tested object. The numerical tests concerned a fragment of an elevator used in the “Bryza” aircraft. The tests were conducted for networks with radial basis functions [RBF] and multilayer perceptrons [MLP]. The calculations described in the paper are an attempt at testing the FEM - ANN system with respect to design optimization.

A New Approach to Evaluating Business Ethics

2011 ◽  
pp. 23-39
Author(s):  
Mo Adam Mahmood ◽  
Gary L. Sullivan ◽  
Ray-Lin Tung
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Business Ethics ◽  
Ethical Decision ◽  
Ethical Decision Making ◽  
Exploratory Research ◽  
Multiple Discriminant Analysis ◽  
Comparative Performance ◽  
New Approach ◽  
Artificial Neural

Stimulated by recent high-profile incidents, concerns about business ethics have increased over the last decade. In response, research has focused on developing theoretical and empirical frameworks to understand ethical decision making. So far, empirical studies have used traditional quantitative tools, such as regression or multiple discriminant analysis (MDA), in ethics research. More advanced tools are needed. In this exploratory research, a new approach to classifying, categorizing and analyzing ethical decision situations is presented. A comparative performance analysis of artificial neural networks, MDA and chance showed that artificial neural networks predict better in both training and testing phases. While some limitations of this approach were noted, in the field of business ethics, such networks are promising as an alternative to traditional analytic tools like MDA.

Characterization of Mexican coffee according to mineral contents by means of multilayer perceptrons artificial neural networks

2014 ◽  
Vol 34 (1) ◽  
pp. 7-11 ◽  
Author(s):  
Roberto Muñiz-Valencia ◽  
José M. Jurado ◽  
Silvia G. Ceballos-Magaña ◽  
Ángela Alcázar ◽  
Julio Hernández-Díaz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multilayer Perceptrons ◽  
Mineral Contents ◽  
Artificial Neural
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