Efficient continuous-time asymmetric Hopfield networks for memory retrieval.
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.
Institute of Systems Engineering, Tianjin University, 300072, China. zhengpengsheng@yahoo.cn
Pengsheng
P
Zheng
Wansheng
W
Tang
Jianxiong
J
Zhang
D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001245:Association Learning / Q000502:physiology; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D055641:Mathematical Concepts; D008568:Memory / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009435:Synaptic Transmission / Q000502:physiology; D013997:Time Factors