RETRACTED ARTICLE: Big Data Mining and Analysis Based on Convolutional Fuzzy Neural Network

Abstract

The purpose of this paper is to solve the shortcomings of traditional big data mining and analysis (BDMA); this paper compares and analyzes the denoise effect of wavelet transform and wavelet packet analysis and chooses to use wavelet packet analysis method to denoise the signal and extract fault feature vector. Finally, the extracted feature vectors are divided into two libraries, including training and testing. The fuzzy convolutional neural network (FCNN) is trained by using the training sample database data, the network weights are constantly modified, and the projection of nonlinear is computed between the input features and output features. After the expected recognition accuracy is achieved, the performance of FCNN is evaluated by using the detection samples. The performance comparison and analysis are conducted with the traditional BDMA algorithm, mainly including the comparison of recognition accuracy and learning convergence speed. Two stages of feature extraction and data mining (DM) are constructed on the big data set. The experimental results prove the effectiveness of wavelet transform feature and FCNN in analyzing and mining in big data. Finally, through the example of BDMA, this paper verifies that the organic combination of wavelet transform feature extraction and FCNN algorithm obviously improves the efficiency, meets the requirements of big data analysis, and provides potential application value for BDMA.