EXPONENTIAL STABILITY OF CONTINUOUS BAM NEURAL NETWORK
连续型BAM神经网络的指数稳定性

In this paper, the continuous bidirectional associative memory(BAM) neural networks can be considered as a special Hopfield network model. A novel exponential stability analysis is presented for the equilibrium points of continuous BAM neural networks. A constraint condition on the connection matrix has been found under which the neural network has a unique equilibrium point. The analysis in this paper can be used to design globally exponentially stable neural networks.