Название: Complex-Valued Neural Networks Systems With Time Delay: Stability Analysis and Anti-Synchronization Control Автор: Ziye Zhang, Zhen Wang, Jian Chen, Chong Lin
Издательство: Springer
Год: 2022
Страниц: 236
Язык: английский
Формат: pdf (true)
Размер: 10.19 MB
In recent years, with the emergence of new scientific and technological methods, complex signals have appeared in radar, imaging and other fields, which makes the state variables of the controlled system extended from the real number domain to the complex number one. As a result, the complex-valued neural networks (CVNNs) model has entered scholars’ vision. It is a kind of network that processes information with complex parameters and variables in a complex field. Compared with real-valued neural networks, on one hand, the neuron state, activation function and weight of complex-valued ones are complex-valued, which can directly process two-dimensional data. On the other hand, CVNNs model is more efficient, faster and has better generalization ability. In particular, it can deal with many problems that cannot be solved by real-valued neural networks. So far, CVNNs have been widely used in many biological and engineering fields, such as image processing, optoelectronics, pattern recognition, signal processing and so on. Moreover, time delay is inevitable in the actual system, which has a very significant impact on the system. It often leads to the delay in information transmission, affects the performance of the system and even causes instability in the system. Therefore, it is of great significance to make a profound study of CVNNs model with time delay.
This book provides up-to-date developments in the stability analysis and (anti-)synchronization control area for complex-valued neural networks systems with time delay. It brings out the characteristic systematism in them and points out further insight to solve relevant problems. It presents a comprehensive, up-to-date, and detailed treatment of dynamical behaviors including stability analysis and (anti-)synchronization control. The materials included in the book are mainly based on the recent research work carried on by the authors in this domain.
The book is suitable for postgraduates, senior undergraduates, graduate students and senior researchers interested in or working with the system theory of complex-valued neural networks, or for those relevant scientific and technical workers with research in stability and control theory in some areas containing engineering disciplines, applied mathematics, control engineering, system analysis and integration, automation, nonlinear science and Computer Science. The prerequisites for this book are modest, and it is assumed that the readers of this book have some background in related issues involving neural networks, system control theory and matrix theory.
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