Institute of Systems Engineering, Tianjin University, 300072, China.
Neural Comput. 2010 Jun;22(6):1597-614. doi: 10.1162/neco.2010.05-09-1014.
A novel m energy functions method is adopted to analyze the retrieval property of continuous-time asymmetric Hopfield neural networks. Sufficient conditions for the local and global asymptotic stability of the network are proposed. Moreover, an efficient systematic procedure for designing asymmetric networks is proposed, and a given set of states can be assigned as locally asymptotically stable equilibrium points. Simulation examples show that the asymmetric network can act as an efficient associative memory, and it is almost free from spurious memory problem.
采用一种新的 m 能量函数方法分析连续时间非对称 Hopfield 神经网络的检索特性。提出了网络局部和全局渐近稳定性的充分条件。此外，还提出了一种设计非对称网络的有效系统程序，可以将给定的状态集指定为局部渐近稳定平衡点。仿真示例表明，非对称网络可以作为有效的联想记忆，几乎没有虚假记忆问题。
