几类混沌神经网络的同步控制理论与方法

　　Chaos theory is an important content in nonlinear sciences and has applications in various fields such as biology， economics， secure communication， and engineering systems. As a branch of nonlinear science， the neural network has complex dynamic behaviors， such as instability， bifurcation and chaos. With the rapid development of modern science and technology， and the wide application of chaos synchronization，chaos synchronization control of neural networks has attracted wide attention in the academic community.The book is divided into six chapters. In Chapter 1， we introduce the background of chaotic neural networks. Chapter 2 investigates the problem of designing sampled-data controller for master-slave synchronization of chaotic Lure systems with time delay. The problem of the sampled-data synchronization control for delayed chaotic neural networks via free-matrix-based time-dependent discontinuous Lyapunov functional approach is discussed in Chapter 3.Chapter 4 proposes a new discontinuous Lyapunov functional approach to research sampled-data synchronization control of chaotic neural networks with mixed delays. In Chapter 5， we mainly discuss the exponential synchronization problem of chaotic neural networks with mixed delays via the sampled-data control. The mean square delay-distribution-dependent exponential synchronization problem of Markovian jumpingdiscrete-time chaotic neural networks with random delays is investigated in Chapter 6.

　　海泉，工学博士，内蒙古师范大学数学科学学院教师，运筹学与控制论专业的硕士研究生导师。从事有关复杂系统的定性理论，混沌、分岔控制与图像加密相关的研究工作。发表7篇SCI论文， 出版1部专著。