Exponential stabilization of delayed recurrent neural networks: A state estimation based approach.
This paper is concerned with the stabilization problem of delayed recurrent neural networks. As the states of neurons are usually difficult to be fully measured, a state estimation based approach is presented. First, a sufficient condition is derived such that the augmented system under consideration is globally exponentially stable. Then, by employing a decoupling technique, the gain matrices of the controller and state estimator are achieved by solving some linear matrix inequalities. Finally, a delayed neural network with chaotic behaviors is exploited to demonstrate the applicability of the developed result.
School of Electronics and Information Engineering, Soochow University, Suzhou 215006, PR China. Electronic address: cshhuang@gmail.com.
He
H
Huang
Tingwen
T
Huang
Xiaoping
X
Chen
Chunjiang
C
Qian
D000465:Algorithms; D003198:Computer Simulation; D016014:Linear Models; D008390:Markov Chains; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics
Decoupling; Exponential stabilization; Recurrent neural networks; State estimation; Time delay