Utilization of TiungSAT-1 data for modelling sea surface current
This study introduces a new approach for operational TiungSAT-1 data for modeling coastal current pattern. The Hopfield neural network was used to model sea surface current movements. In matching process using Hopfield neural network, identified features have to be mathematically compared to each ot...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2007
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/4774/1/31-Utilization_of_Tiungsat.pdf http://eprints.utm.my/id/eprint/4774/ |
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| Summary: | This study introduces a new approach for operational TiungSAT-1 data for modeling coastal current pattern. The Hopfield neural network was used to model sea surface current movements. In matching process using Hopfield neural network, identified features have to be mathematically compared to each other in order to build an energy function that will be minimized. In this context, the neuron network has been taken in two dimensions; raw and column in order to match between the similar feature of surface pattern, it was required that the two features were extracted from the same location. The Euler method is used to minimized the
energy function of neuron equation. The study shows that the surface current pattern can be modeled by high accuracy of ±0.14 m/s. |
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