Zhang J, Jin X
Traction Power National Laboratory, Southwest Jiaotong University, Chengdu, People's Republic of China.
Neural Netw. 2000 Sep;13(7):745-53. doi: 10.1016/s0893-6080(00)00050-2.
In this paper, without assuming the boundedness, monotonicity and differentiability of the activation functions, we present new conditions ensuring existence, uniqueness, and global asymptotical stability of the equilibrium point of Hopfield neural network models with fixed time delays or distributed time delays. The results are applicable to both symmetric and nonsymmetric interconnection matrices, and all continuous nonmonotonic neuron activation functions.
在本文中,我们在不假设激活函数有界性、单调性和可微性的情况下,给出了确保具有固定时滞或分布时滞的霍普菲尔德神经网络模型平衡点的存在性、唯一性和全局渐近稳定性的新条件。这些结果适用于对称和非对称互连矩阵,以及所有连续非单调的神经元激活函数。