Abstract
For the complexity of the automation system roll on, sensors should have to be more intelligent. Recently, Neural Network is widely used to intelligentize sensors for its well performance on capturing the information of the data. But due to its intrinsic linear character, it doesn't perform well in nonlinear data processing. In this paper, RNN with Kernel Principal Component Analysis (KPCA) and Principal Component Analysis (PCA) as the feature extraction is introduced in as comparison. And then an experimental system is set up with pressure sensor. By examining the data of the example, it is shown that the proposed methods can both achieve good performance comparing with NN method. And the KPCA method performs better than the PCA method.
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