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- W2370307849 abstract "The temperature drift error is one of the major errors of the displacement sensor system.In order to improve the accuracy and stability,the output from the displacement sensor and the one from the temperature sensor can be fused.At present,neural network has been widely applied to the area of data fusion,in which data from multiple sensors are mostly fused by BP neural network.The low training speed of BP network leads to the poor practicability,therefore in this paper,a data fusion method based on RBF neural network is proposed for reducing temperature drift error.The outputs from the displacement sensor and those from the temperature sensor are sent to the fusion center,where the RBF neural network is trained and as a result the stable output is obtained.Experiments show that the output stability of the displacement sensor is improved by 4 times under the same temperature fluctuation.It is obvious that the proposed method in this paper is effective for reducing the drift error of displacement sensor system." @default.
- W2370307849 created "2016-06-24" @default.
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- W2370307849 date "2006-01-01" @default.
- W2370307849 modified "2023-09-24" @default.
- W2370307849 title "Application of Data Fusion Based on Neural Network for Displacement Sensing System" @default.
- W2370307849 hasPublicationYear "2006" @default.
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