Research on improved wavelet convolutional wavelet neural networks

Tian-Yue Li,Ying-Xiao Guo,Fang-Ling Zuo,Jia-Ming Chen,Jing-Wei Liu

doi:10.1007/s10489-020-02015-5

Abstract

Convolutional neural network (CNN) is recognized as state of the art of deep learning algorithm, which has a good ability on the image classification and recognition. The problems of CNN are as follows: the precision, accuracy and efficiency of CNN are expected to be improved to satisfy the requirements of high performance. The main work is as follows: Firstly, wavelet convolutional neural network (wCNN) is proposed, where wavelet transform function is added to the convolutional layers of CNN. Secondly, wavelet convolutional wavelet neural network (wCwNN) is proposed, where fully connected neural network (FCNN) of wCNN and CNN are replaced by wavelet neural network (wNN). Thirdly, image classification experiments using CNN, wCNN and wCwNN algorithms, and comparison analysis are implemented with MNIST dataset. The effect of the improved methods are as follows: (1) Both precision and accuracy are improved. (2) The mean square error and the rate of error are reduced. (3) The complexitie of the improved algorithms is increased.

Highlights

Convolutional neural network (CNN) is a typical deep learning method which is based on feature extraction of convolution calculation [9]
All the mean square error (MSE) in each Adjustment cycle (AC) are recorded, which is shown in Fig. 8: Statistic results of the above simulations are listed in Table 3, which includes: (1) the final MSE of each Simulation process (SP), (2) the error rate of each SP, (3) Consumed time of each SP
MNIST dataset is adopted to verify the proposed methods in this study, and CNN is implemented to finish the Numbers of SPs and Total statistical item

Summary

Introduction

Convolutional neural network (CNN) is a typical deep learning method which is based on feature extraction of convolution calculation [9]. It is widely applied to fields of prediction, classification [14] etc. CNN can solve highdimensional problems which are difficult for traditional machine learning methods [19]. The ability to minimize the system error between the label and the inference [22] of CNN is much more powerful especially in the application of image processing. The neuron weights [12] of CNN are modified by forward propagation and error back propagation [15].

Methods

Results

Discussion

Conclusion