Two-Layer Feedback Neural Networks with Associative Memories  

作　　者：吴桂坤 赵鸿 

机构地区：[1]Department of Physics, and Institute of Theoretical Physics and Astrophysics, Xiamen University, Xiamen 361005

出　　处：《Chinese Physics Letters》2008年第11期3871-3874,共4页中国物理快报（英文版）

基　　金：Supported by the National Natural Science Foundation of China under Grant No 10475067 and National Basic Research Programme of China under Grant No 2007CB814800.

摘　　要：We construct a two-layer feedback neural network by a Monte Carlo based algorithm to store memories as fixed-point attractors or as limit-cycle attractors. Special attention is focused on comparing the dynamics of the network with limit-cycle attractors and with fixed-point attractors. It is found that the former has better retrieval property than the latter. Particularly, spurious memories may be suppressed completely when the memories are stored as a long-limit cycle. Potential application of limit-cycle-attractor networks is discussed briefly.We construct a two-layer feedback neural network by a Monte Carlo based algorithm to store memories as fixed-point attractors or as limit-cycle attractors. Special attention is focused on comparing the dynamics of the network with limit-cycle attractors and with fixed-point attractors. It is found that the former has better retrieval property than the latter. Particularly, spurious memories may be suppressed completely when the memories are stored as a long-limit cycle. Potential application of limit-cycle-attractor networks is discussed briefly.

关 键 词：the power-law exponents PRECIPITATION durative abrupt precipitation change 

分 类 号：O4[理学—物理]