1. Research on improved wavelet convolutional wavelet neural networks
- Author
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Ying-Xiao Guo, Jiaming Chen, Fang-Ling Zuo, Jingwei Liu, and Tianyue Li
- Subjects
0303 health sciences ,Mean squared error ,Artificial neural network ,Contextual image classification ,Computer science ,business.industry ,Deep learning ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Wavelet transform ,Pattern recognition ,02 engineering and technology ,Convolutional neural network ,03 medical and health sciences ,Wavelet ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,MNIST database ,030304 developmental biology - 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.
- Published
- 2020