Global Attractivity of Discrete-Time Recurrent Neural Networks With Lnsaturating Piecewise Linear Activation Functions

Multistable networks have attracted much interests in recent years, since the monostable networks are computationally restricted. This paper studies the global attractivity of a class of discrete-time recurrent neural networks with unsaturating piecewise linear activation function. Some conditions are derived by mathematical analysis to guarantee the boundedness and global attractivity of the networks. Simulation examples are used to illustrate the theory developed in this paper