Accurate Estimation of Time Histories for Improved Durability Prediction Using Artificial Neural Networks

2012 ◽  
Author(s):  
Sivakumar Balakrishnan ◽  
Anoop PP ◽  
Ravindra V Kharul ◽  
Sasun C
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Accurate Estimation ◽  
Durability Prediction ◽  
Time Histories ◽  
Artificial Neural ◽  
Estimation Of Time

scholarly journals The Use of Artificial Neural Networks to Determine the Engine Power and Fuel Consumption of Modern Bulk Carriers, Tankers and Container Ships

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4827
Author(s):  
Tomasz Cepowski ◽  
Paweł Chorab
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Consumption ◽  
Design Stage ◽  
Accurate Estimation ◽  
Ship Propulsion ◽  
Fuel Prices ◽  
Bulk Carriers ◽  
Artificial Neural ◽  
Engine Power

The 2007–2008 financial crisis, together with rises in fuel prices and stringent pollution regulation, led to the need to update the methods concerning ship propulsion system design. In this article, a set of artificial neural networks was used to update the design equations to estimate the engine power and fuel consumption of modern tankers, bulk carriers, and container ships. Deadweight or TEU capacity and ship speed were used as the inputs for the ANNs. This study shows that even a linear ANN with two neurons in the input and output layers, with purelin activation functions, offers an accurate estimation of ship propulsion parameters. The proposed linear ANNs have simple mathematical structures and are straightforward to apply. The ANNs presented in the article were developed based on the data of the most recent ships built from 2015 to present, and could have a practical application at the preliminary design stage, in transportation or air pollution studies for modern commercial cargo ships. The presented equations mirror trends found in the literature and offer much greater accuracy for the features of new-built ships. The article shows how to estimate CO2 emissions for a bulk carrier, tanker, and container carrier utilizing the proposed ANNs.

The Role of Neural Networks and Metaheuristics in Agile Software Development Effort Estimation

2020 ◽  
Vol 11 (2) ◽  
pp. 50-71 ◽  
Author(s):  
Anupama Kaushik ◽  
Devendra Kumar Tayal ◽  
Kalpana Yadav
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Software Development ◽  
Agile Software Development ◽  
Accurate Estimation ◽  
Effort Estimation ◽  
Whale Optimization ◽  
Artificial Neural ◽  
Agile Software

In any software development, accurate estimation of resources is one of the crucial tasks that leads to a successful project development. A lot of work has been done in estimation of effort in traditional software development. But, work on estimation of effort for agile software development is very scant. This paper provides an effort estimation technique for agile software development using artificial neural networks (ANN) and a metaheuristic technique. The artificial neural networks used are radial basis function neural network (RBFN) and functional link artificial neural network (FLANN). The metaheuristic technique used is whale optimization algorithm (WOA), which is a nature-inspired metaheuristic technique. The proposed techniques FLANN-WOA and RBFN-WOA are evaluated on three agile datasets, and it is found that these neural network models performed extremely well with the metaheuristic technique used. This is further empirically validated using non-parametric statistical tests.

Prediction of Viscosity of Nanofluids Using Artificial Neural Networks

2014 ◽  
Author(s):  
Ningbo Zhao ◽  
Shuying Li ◽  
Zhitao Wang ◽  
Yunpeng Cao
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rbf Neural Network ◽  
Accurate Estimation ◽  
Rbf Neural Networks ◽  
Ann Model ◽  
Model Based ◽  
Viscosity Prediction ◽  
Artificial Neural ◽  
Prediction Approach

The viscosity of nanofluids can be affected by many factors. In pursuit of such improved accuracy, model-based viscosity prediction methods have become more complicated. Therefore, there is a need to find an alternative approach that is able to provide a quick solution to viscosity prediction for nanofluids. In this paper, a novel viscosity prediction approach using artificial neural networks (ANN) is introduced as an alternative to the model-based viscosity prediction approach to provide a quick and accurate estimation of nanofluids viscosity. Radial basis function (RBF) neural networks has been utilized to form viscosity prediction architectures. Alumina (Al2O3)-water nanofluids from existing literatures were used to test the effectiveness of the proposed method. The results showed that RBF neural network model had a reasonable agreement in predicting experimental data. The findings of this paper indicated that the ANN model was an effective method for prediction of the viscosity of nanofluids and had better prediction accuracy and simplicity compared with the other existing theoretical methods.

The accurate estimation of physicochemical properties of ternary mixtures containing ionic liquids via artificial neural networks

2015 ◽  
Vol 17 (6) ◽  
pp. 4533-4537 ◽  
Author(s):  
John C. Cancilla ◽  
Pablo Díaz-Rodríguez ◽  
Gemma Matute ◽  
José S. Torrecilla
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ionic Liquids ◽  
Physicochemical Properties ◽  
Ternary Mixture ◽  
Ternary Mixtures ◽  
Accurate Estimation ◽  
Mathematical Tool ◽  
Artificial Neural

A graphic scheme of the mathematical tool designed is able to estimate physicochemical properties of a ternary mixture.

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