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- W4225544987 abstract "It’s important to evaluate the induced magnetic signature of ferromagnetic vessels in demagnetization practice. Among all the components of induced magnetic field, ship’s vertical component of vertical induced field (Ziz) is always hard to be measured by traditional geomagnetic simulation method, which exists many deficiencies just like high hardware requirements and low measurement accuracy. To avoid this deficiency, genetic neural network is proposed in this work. This approach is a mixture of genetic algorithm and BP neural network, which can predict the induced magnetic signature by training synthetic data and learning the relationship between inputs and outputs. A simulation and physical model experiment are designed to verify the proposed approach. The experimental results show that the root mean square error between predicted values and measured values is less than 10%. Such accuracy is quite acceptable in ship’s demagnetization practice. The proposed approach can be treated as a viable alternative to traditional geomagnetic simulation method." @default.
- W4225544987 created "2022-05-05" @default.
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- W4225544987 date "2022-01-01" @default.
- W4225544987 modified "2023-10-16" @default.
- W4225544987 title "Prediction on the Induced Magnetic Signature of Ships Using Genetic Neural Network" @default.
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- W4225544987 doi "https://doi.org/10.1007/978-981-19-1870-4_64" @default.
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