Research and Application Based on the Combination of Data Analysis and the Deep Neural Network
A regularized elastic net is a powerful deep learning model,which can combine the characteristics of linear regression and logistic regression to perform both feature selection and parameter control,avoiding some of the limitations of traditional regularization.Discrete Fourier transform feature extraction is a commonly-used signal processing method that can extract specific frequency features from signals,and it has a wide range of applications in many fields.This article combines the signal analysis technology of elastic net regularization and windowed discrete Fourier transform to conduct corresponding research and applications.This article takes the vibration data of faulty bearings from Case Western Reserve University as an example,performs signal analysis and processing,and then obtains an elastic net model with high accuracy through the learning and predicting of the neural network model.This method is of great research value for many complex problems.
Deep neural network modelRegularized elastic netData analysis toolSignal processing
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