The book aims to solve the discrete implementation problems of continuous-time neural network models while improving the performance of neural networks by using various Zhang Time Discretization (ZTD) formulas.

�ber die Autorin bzw. den Autor:

Yunong Zhang, PH.D., earned his B.S. degree from Huazhong University of Science and Technology, Wuhan, China, in 1996, his M.S. degree from South China University of Technology, Guangzhou, China, in 1999, and his Ph.D. from the Chinese University of Hong Kong, Shatin, Hong Kong, China, in 2003. He is currently a professor at the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China. Dr. Zhang was supported by the Program for New Century Excellent Talents in Universities in 2007. He received the Best Paper Award from the International Symposium on Systems and Control in Aeronautics and Astronautics (ISSCAA) in 2008 and the Best Paper Award from the International Conference on Automation and Logistics (ICAL) in 2011. He was among the Highly Cited Scholars of China selected and published by Elsevier from 2014 to 2022.

Jinjin Guo, Ph.D., earned her B.E. degree in measurement technology and instrument from Nanchang University, Nanchang, China, in 2016, her M.E. degree in control engineering from Sun Yat-sen University, Guangzhou, China, in 2018, and her Ph.D. in computer science and technology from Sun Yat-sen University, Guangzhou, China, in 2022. She is currently a lecturer at the School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China. Her main research interests include neural networks, numerical computation, and tracking control.

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