
Zhang Time Discretization (ZTD) Formulas and Applications
Description
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The authors summarize and present the systematic derivations and complete research of ZTD formulas from special 3S-ZTD formulas to general NS-ZTD formulas. These finally lead to their proposed discrete-time Zhang neural network (DTZNN) algorithms, which are more efficient, accurate, and elegant. This book will open the door to scientific and engineering applications of ZTD formulas and neural networks, and will be a major inspiration for studies in neural network modeling, numerical algorithm design, prediction, and robot manipulator control.
The book will benefit engineers, senior undergraduates, graduate students, and researchers in the fields of neural networks, computer mathematics, computer science, artificial intelligence, numerical algorithms, optimization, robotics, and simulation modeling.
Reviews / Votes
``For those interested in exploring and handling the intricacies of discrete time-dependent problems, this book may offer a comprehensive and thought-provoking journey. It perhaps deserves serious and much consideration from academics and researchers in the fields.''Professor Zibin Zheng, IEEE Fellow, Sun Yat-sen University, China
``The book is appealing for graduate students as well as academic and industrial researchers. Based on the systematic research of new effective time-discretization formulas, the book may generate curiosity and also happiness to its readers for learning more in the fields and researches.''
Professor Shuai Li, University of Oulu, Finland
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Persons
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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