Contents I. Introduction
Neural networks have been intensively studied in the past decade and have been used in various applications such as designing associative memories and solving optimization problems. When a neural network is designed to solve optimization problems, the designed neural network must have a unique equilibrium point that is globally asymptotically stable. Therefore, it is of great interest to establish conditions that ensure the global asymptotic stability of a unique equilibrium point of a neural network. Recently, many researchers have studied the equilibria and stability properties of neural networks and presented various sufficient conditions for the uniqueness and global asymptotic stability of the equilibrium point of different classes of neural networks [1]–[10]. On the other hand, the delayed version of neural networks have also proved to be important for solving some classes of motion-related optimization problems. Some results concerning the dynamical behavior of neural networks with delay has been reported in [11]–[22]. In some of the recent research papers, researchers have paid a particular attention to the stability analysis of bidirectional associative memory (BAM) neural networks with time delays as this class of neural networks shown to be useful network model for applications in pattern recognition, solving optimization problems and automatic control engineering [23]– [33]. Some useful results on the uniqueness and global asymptotic stability of the equilibrium point for BAM neural networks with delays can be found in [23]–[33].
