New results on anti-synchronization of switched neural networks with time-varying delays and lag signals.
This paper investigates the problem of global exponential anti-synchronization of a class of switched neural networks with time-varying delays and lag signals. Considering the packed circuits, the controller is dependent on the output of the system as the inner states are very hard to measure. Therefore, it is necessary to investigate the controller based on the output of the neuron cell. Through theoretical analysis, it is obvious that the obtained ones improve and generalize the results derived in the previous literature. To illustrate the effectiveness, a simulation example with applications in image encryptions is also presented in the paper.
School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China.
Yuting
Y
Cao
School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China. Electronic address: wenshiping226@126.com.
Shiping
S
Wen
The Department of Mechanical Engineering, The University of Hong Kong, Hong Kong.
Michael Z Q
MZ
Chen
Texas A & M University at Qatar, Doha 5825, Qatar.
Tingwen
T
Huang
School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China.
Zhigang
Z
Zeng
D000465:Algorithms; D016494:Computer Security / Q000639:trends; D003198:Computer Simulation / Q000639:trends; D016571:Neural Networks, Computer; D009474:Neurons; D017711:Nonlinear Dynamics; D013997:Time Factors
Anti-synchronization; Exponential stability; Switched neural networks