Zeroing Dynamics, Gradient Dynamics, and Newton Iterations
1st Edition
Published on 9. October 2018
340 pages
978-1-4987-5379-1 (ISBN)
Description
Neural networks and neural dynamics are powerful approaches for the online solution of mathematical problems arising in many areas of science, engineering, and business. Compared with conventional gradient neural networks that only deal with static problems of constant coefficient matrices and vectors, the authors' new method called zeroing dynamics solves time-varying problems.
Zeroing Dynamics, Gradient Dynamics, and Newton Iterations is the first book that shows how to accurately and efficiently solve time-varying problems in real-time or online using continuous- or discrete-time zeroing dynamics. The book brings together research in the developing fields of neural networks, neural dynamics, computer mathematics, numerical algorithms, time-varying computation and optimization, simulation and modeling, analog and digital hardware, and fractals.
The authors provide a comprehensive treatment of the theory of both static and dynamic neural networks. Readers will discover how novel theoretical results have been successfully applied to many practical problems. The authors develop, analyze, model, simulate, and compare zeroing dynamics models for the online solution of numerous time-varying problems, such as root finding, nonlinear equation solving, matrix inversion, matrix square root finding, quadratic optimization, and inequality solving.
Zeroing Dynamics, Gradient Dynamics, and Newton Iterations is the first book that shows how to accurately and efficiently solve time-varying problems in real-time or online using continuous- or discrete-time zeroing dynamics. The book brings together research in the developing fields of neural networks, neural dynamics, computer mathematics, numerical algorithms, time-varying computation and optimization, simulation and modeling, analog and digital hardware, and fractals.
The authors provide a comprehensive treatment of the theory of both static and dynamic neural networks. Readers will discover how novel theoretical results have been successfully applied to many practical problems. The authors develop, analyze, model, simulate, and compare zeroing dynamics models for the online solution of numerous time-varying problems, such as root finding, nonlinear equation solving, matrix inversion, matrix square root finding, quadratic optimization, and inequality solving.
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Product notice
Reflowable
Illustrations
22 Tables, black and white; 294 Illustrations, black and white
ISBN-13
978-1-4987-5379-1 (9781498753791)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Yunong Zhang | Lin Xiao | Zhengli Xiao
Zeroing Dynamics, Gradient Dynamics, and Newton Iterations
Book
04/2026
1st Edition
CRC Press
€84.36
Shipment within 10-20 days
Yunong Zhang | Lin Xiao | Zhengli Xiao
Zeroing Dynamics, Gradient Dynamics, and Newton Iterations
Book
12/2015
1st Edition
CRC Press
€222.84
Shipment within 15-20 days
Persons
Yunong Zhang is a professor in the School of Information Science and Technology at Sun Yat-sen University. He is also with the SYSU-CMU Shunde International Joint Research Institute for cooperative research. He has published more than 375 scientific works of various types and has been a winner of the Best Paper Award of ISSCAA and the Best Paper Award of ICAL. He was among the 2014 Highly Cited Scholars of China. His main research interests include neural networks, robotics, computation, and optimization. He earned a PhD from the Chinese University of Hong Kong.
Lin Xiao is a lecturer in the College of Information Science and Engineering at Jishou University. His current research interests include neural networks, intelligent information processing, robotics, and related areas. He earned a PhD from Sun Yat-sen University.
Zhengli Xiao is currently pursuing an MS in the Department of Computer Science in the School of Information Science and Technology at Sun Yat-sen University. He is also with the SYSU-CMU Shunde International Joint Research Institute for cooperative research. His current research interests include neural networks, intelligent information processing, and learning machines. He earned a BS in software engineering from Changchun University of Science and Technology.
Mingzhi Mao is an associate professor in the School of Information Science and Technology at Sun Yat-sen University. His main research interests include intelligence algorithms, software engineering, and management information systems. He earned a PhD from the Department of Computer Science at Sun Yat-sen University.
Lin Xiao is a lecturer in the College of Information Science and Engineering at Jishou University. His current research interests include neural networks, intelligent information processing, robotics, and related areas. He earned a PhD from Sun Yat-sen University.
Zhengli Xiao is currently pursuing an MS in the Department of Computer Science in the School of Information Science and Technology at Sun Yat-sen University. He is also with the SYSU-CMU Shunde International Joint Research Institute for cooperative research. His current research interests include neural networks, intelligent information processing, and learning machines. He earned a BS in software engineering from Changchun University of Science and Technology.
Mingzhi Mao is an associate professor in the School of Information Science and Technology at Sun Yat-sen University. His main research interests include intelligence algorithms, software engineering, and management information systems. He earned a PhD from the Department of Computer Science at Sun Yat-sen University.
Content
Time-Varying Root Finding. Nonlinear Equation Solving. Matrix Inversion. Matrix Square Root Finding. Time-Varying Quadratic Optimization. Time-Varying Inequality Solving. Application to Fractal.
