中国物理B ›› 2016, Vol. 25 ›› Issue (12): 120701-120701.doi: 10.1088/1674-1056/25/12/120701
Synthesization of high-capacity auto-associative memories using complex-valued neural networks
Yu-Jiao Huang(黄玉娇), Xiao-Yan Wang(汪晓妍), Hai-Xia Long(龙海霞), Xu-Hua Yang(杨旭华)
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Synthesization of high-capacity auto-associative memories using complex-valued neural networks
Yu-Jiao Huang(黄玉娇), Xiao-Yan Wang(汪晓妍), Hai-Xia Long(龙海霞), Xu-Hua Yang(杨旭华)
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
摘要：
In this paper, a novel design procedure is proposed for synthesizing high-capacity auto-associative memories based on complex-valued neural networks with real-imaginary-type activation functions and constant delays. Stability criteria dependent on external inputs of neural networks are derived. The designed networks can retrieve the stored patterns by external inputs rather than initial conditions. The derivation can memorize the desired patterns with lower-dimensional neural networks than real-valued neural networks, and eliminate spurious equilibria of complex-valued neural networks. One numerical example is provided to show the effectiveness and superiority of the presented results.
中图分类号: (Neural networks, fuzzy logic, artificial intelligence)
- 07.05.Mh