Volume 29 Issue 6
Dec.  2020
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WU Wei, JING Xiaoyuan, DU Wencai. The Kernel Dynamics of Convolutional Neural Networks in Manifolds[J]. Chinese Journal of Electronics, 2020, 29(6): 1185-1192. doi: 10.1049/cje.2020.10.004
Citation: WU Wei, JING Xiaoyuan, DU Wencai. The Kernel Dynamics of Convolutional Neural Networks in Manifolds[J]. Chinese Journal of Electronics, 2020, 29(6): 1185-1192. doi: 10.1049/cje.2020.10.004

The Kernel Dynamics of Convolutional Neural Networks in Manifolds

doi: 10.1049/cje.2020.10.004
Funds:  This work was supported by the National Natural Science Foundation of China (No.61933013, No.U1736211), the Strategic Priority Research Program of the Chinese Academy of Sciences (No.XDA22030301), the Foundation of Macau (No.MF1809, No.MF1713).
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  • Corresponding author: JING Xiaoyuan (corresponding author) received the Ph.D. degree in Pattern Recognition and Intelligent System from the Nanjing University of Science and Technology, in 1998. He was a Professor with the Department of Computer, Shenzhen Research Student School, Harbin Institute of Technology, 2005. Now he is a Professor with the School of Computer Science, Wuhan University, China. His research interests include pattern recognition, machine learning, image processing, artificial intelligence, and software engineering. He has published more than 100 papers, such as TIP, TIFS, TSE, TCB, CVPR, AAAI, IJCAI, ICSE and PR. (Email:jingxy_2000@126.com)
  • Received Date: 2019-07-23
  • Publish Date: 2020-12-25
  • We propose a novel expression from manifolds to define Convolutional neural network (CNN). The layered structure is proceeded by integration in limited space continuously, with weights adjusted including value and direction in neural manifolds. Status transfer functions are proposed to simulate the kernel dynamics as a control matrix. We theoretically analyze the stability and controllability of kernel-based CNNs, and verify our findings by numerical experiments.
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