Dynamic Behavior Analysis Of A Class Of Memristor-based Recurrent Neural Networks With Time-varying Delays

Memristor-based neural networks is a kind of neural system structure with unique memory and circuit realization,and it is a new model of neural networks.Therefore,the dynamic analysis of memristor-based recurrent neural networks have attracted the attention of many researchers.The main research contents of this paper as follows:We study the existence,uniqueness and global exponential stability of the equilibrium point for a class of memristor-based recurrent neural networks with time-varying delays.First,by virtue of homomorphic theory,it is proved that memristor-based recurrent neural networks with time-varying delays has a unique equilibrium point.Second,by Lyapunov functional theory and linear matrix inequality,we prove that memristor-based recurrent neural networks with time-varying delays is global exponential stable,and obtain the sufficient conditions for global exponential stability of memristor-based recurrent neural networks with time-varying delays without introducing any parameters,so as to provide guarantee for the design and implementation of the circuit.Finally,numerical examples are employed to indicate the validity of the obtained results.We study the design of the periodical intermittent control for a class of memristor-based recurrent neural networks with time-varying delays.By constructing a suitable Lyapunov functional,we can prove that the equilibrium point of memristor-based recurrent neural network with time-varying delays under the periodical intermittent control is global exponentially stable.The results show that the control period,width,and coefficients in the intermittent controller can be determined by an linear matrix inequality without introducing any parameters.Finally,numerical examples are employed to indicate the validity of the obtained results.We study the design of the periodical intermittent control for a class of memristor-based recurrent neural networks with disturbance and mixed delays.By the Lyapunov functional?linear matrix inequality and the periodical intermittent control theory,designing suitable the control width and the control period so that the equilibrium point of memristor-based recurrent neural networks with disturbance and mixed delays can achieve exponential stability under the intermittent control.Finally,numerical examples are employed to indicate the validity of the obtained results.