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- W3181096792 abstract "An improved RBF neural network based on clustering algorithm for estimating wind speed from X-band marine radar images is proposed. To remove the outliner caused by rainfall or co-frequency noise interference, K-means clustering algorithm is applied to the data related to the wind speed estimation. The filtered outliner removing data is feed into the RBF neural network, hidden layer and extended constants are determined by subtractive clustering, the recursive least squares is used to make the network connection weights. Radar and sensor data collected from the East China Sea are used for validation. Compared to the BP neural network based methods, the accuracy of wind estimation is further improved with a reduction of about 2.82m/s in root square deviation, the proposed method can effectively estimate the wind speed from X-band marine radar image." @default.
- W3181096792 created "2021-07-19" @default.
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- W3181096792 date "2021-05-10" @default.
- W3181096792 modified "2023-10-17" @default.
- W3181096792 title "An Improved RBF Neural Network based on Clustering Algorithm for Estimating Wind Speed from X-band Marine Radar Images" @default.
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- W3181096792 doi "https://doi.org/10.1109/icps49255.2021.9468214" @default.
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