scholarly journals Hopfield neural network and pareto optimal algorithms for retrieving sea surface current from TanDEM-X data

2018 ◽  
Vol 169 ◽  
pp. 012023
Author(s):  
Maged Marghany
Keyword(s):  
Neural Network ◽  
Surface Current ◽  
Hopfield Neural Network ◽  
Sea Surface ◽  
Pareto Optimal ◽  
Optimal Algorithms

scholarly journals Sea Surface Current Velocity Retrieving from TanDAM-X Satellite Data

2021 ◽  
pp. 1-10
Author(s):  
M. Marghany ◽  
J.L. Genderen
Keyword(s):  
Neural Network ◽  
Coastal Waters ◽  
Optimal Solution ◽  
Surface Current ◽  
Hopfield Neural Network ◽  
Pareto Optimal Solution ◽  
Sea Surface ◽  
Pareto Optimal ◽  
Network Algorithm ◽  
Neural Network Algorithm

This is the first investigation for the use of TanDEM-X data, satellite for the Malaysian coastal waters. This aims at utilizing an optimization of the Hopfield neural network to retrieve variation of sea surface current along Malaysian coastal waters. In doing so, a multi-objective evolutionary algorithm based on the Pareto front is used to minimize the error produced due to non-linearity between TanDEM-X data and sea surface movements. This work aimed at retrieving sea surface current from TanDEM-X data along the coastal waters of Malaysia. Two approaches have been implemented, the Hopfield neural network algorithm and Pareto optimal solution. The study shows that the Pareto optimal solution has a higher performance than the Hopfield neural network algorithm with a lower RMSE of ±0.009. Furthermore, a Pareto optimal solution can determine the sea surface current pattern variation along the coastal water from TanDEM-X data. In conclusion, TanDEM-X data shows an excellent promise for retrieving sea surface currents.

scholarly journals Prediction of geostrophic currents using big weather data archive and neural networks for the Aegean Sea

2019 ◽  
Vol 9 (1) ◽  
pp. 10-20
Author(s):  
Timur İnan ◽  
Ahmet Fevzi BABA
Keyword(s):  
Neural Network ◽  
Surface Current ◽  
Aegean Sea ◽  
Current Data ◽  
Sea Surface ◽  
Weather Data ◽  
Time Of Arrival ◽  
Surface Currents ◽  
Data Archive ◽  
Wave Direction

Prediction of sea and weather environment variables like wind speed, wind direction, wave height, wave direction, sea surface current direction and magnitude has always been an important subject in marine engineering as they effect on ship speed and effect the time of arrival to destination point as well. In this study, we propose a neural network that can predict the latitudinal and longitudinal components of sea surface currents in the Aegean Sea. The system can predict the sea surface currents components using the wind components which are gathered from the INMARSAT weather report system. The neural network is trained using the historical data which is gathered from UCAR historical weather database and historical surface current data which is gathered from IFREMER database. Keywords: Sea surface current, weather report, prediction, neural network, big data archive.

Augmented Lagrange Hopfield Network for Combined Economic and Emission Dispatch with Fuel Constraint

2017 ◽  
pp. 221-255
Author(s):  
Vo Ngoc Dieu ◽  
Tran The Tung
Keyword(s):  
Neural Network ◽  
Lagrange Function ◽  
Hopfield Neural Network ◽  
Hopfield Network ◽  
Pareto Optimal ◽  
Sigmoid Function ◽  
Compromise Solution ◽  
Test Results ◽  
Pareto Optimal Front ◽  
Emission Dispatch

This chapter proposes an augmented Lagrange Hopfield network (ALHN) for solving combined economic and emission dispatch (CEED) problem with fuel constraint. In the proposed ALHN method, the augmented Lagrange function is directly used as the energy function of continuous Hopfield neural network (HNN), thus this method can properly handle constraints by both augmented Lagrange function and sigmoid function of continuous neurons in the HNN. For dealing with the bi-objective economic dispatch problem, the slope of sigmoid function in HNN is adjusted to find the Pareto-optimal front and then the best compromise solution for the problem will be determined by fuzzy-based mechanism. The proposed method has been tested on many cases and the obtained results are compared to those from other methods available the literature. The test results have shown that the proposed method can find good solutions compared to the others for the tested cases. Therefore, the proposed ALHN could be a favourable implementation for solving the CEED problem with fuel constraint.

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