Analysis and design of asymmetric Hopfield networks with discrete-time dynamics.
The retrieval properties of the asymmetric Hopfield neural networks (AHNNs) with discrete-time dynamics are studied in this paper. It is shown that the asymmetry degree is an important factor influencing the network dynamics. Furthermore, a strategy for designing AHNNs of different sparsities is proposed. Numerical simulations show that AHNNs can perform as well as symmetric ones, and the diluted AHNNs have the virtues of small wiring cost and high pattern recognition quality.
Zheng
Pengsheng
P
Institute of Systems Engineering, Tianjin University, Tianjin 300072, China. pszheng@yahoo.cn
Zhang
Jianxiong
J
Tang
Wansheng
W
D001921:Brain / Q000502:physiology; D008568:Memory / Q000502:physiology; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D013997:Time Factors