Zhang Time Discretization (ZTD) Formulas and Applications (Hardcover)

Yunong Zhang

ISBN 10: 1032806249 / ISBN 13: 9781032806242

Published by Taylor & Francis Ltd, London, 2024

About this Item

Hardcover. This 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.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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781032806242

Bibliographic Details

• Title: Zhang Time Discretization (ZTD) Formulas and Applications (Hardcover)
• Publisher: Taylor & Francis Ltd, London
• Publication Date: 2024
• Language: English
• ISBN 10: 1032806249
• ISBN 13: 9781032806242
• Binding: Hardcover
• Condition: new

About this title

Synopsis

This 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.

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.

About the Author

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.