Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays.
This paper deals with the problem of global exponential synchronization of a class of memristor-based recurrent neural networks with time-varying delays based on the fuzzy theory and Lyapunov method. First, a memristor-based recurrent neural network is designed. Then, considering the state-dependent properties of the memristor, a new fuzzy model employing parallel distributed compensation (PDC) gives a new way to analyze the complicated memristor-based neural networks with only two subsystems. Comparisons between results in this paper and in the previous ones have been made. They show that the results in this paper improve and generalized the results derived in the previous literature. An example is also given to illustrate the effectiveness of the results.
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; Texas A & M University at Qatar, Doha 5825, Qatar.
Shiping
S
Wen
Gang
G
Bao
Zhigang
Z
Zeng
Yiran
Y
Chen
Tingwen
T
Huang
D001185:Artificial Intelligence; D003198:Computer Simulation; D017143:Fuzzy Logic; D008959:Models, Neurological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics
Memristor; Recurrent neural networks; Synchronization; Time-varying delays