带脉冲变系数的BAM神经网络的全局指数稳定性(英文)

Global Exponential Stability of Bam Neural Networks with Varying Coefficient and Impulses

在固定脉冲时刻,利用无需有界、单调和可微的李普希茨激励函数,来研究BAM脉冲神经网络,获得平衡点的存在唯一性和全局指数稳定性的充分条件,然后通过举例来验证所得结论的有效性.

In this paper, some sufficient conditions ensuring existence, uniqueness, and global exponential stability of the equilibrium point of a class of two-layer heteroassociative networks called bidirectional associative memory（BAM） networks with impulses are obtained, which makes full use of Lipschitzian activation functions without assuming their bounded, monotonicity or differentiability and subjected to impulsive state displacements at fixed instants of time. An illustrative example is to demonstrate the effectiveness of the obtained results.