Influence of Blurred Ways on Pattern Recognition of a Scale-Free Hopfield Neural Network  

作　　者：常文利 

机构地区：[1]School of Mathematics, Physics and Software Engineering, Lanzhou Jiaotong University

出　　处：《Communications in Theoretical Physics》2010年第1期195-199,共5页理论物理通讯（英文版）

基　　金：Supported by "Qing Lan" Talent Engineering Funds by Lanzhou Jiaotong University under Grant No. QL-08-18A

摘　　要：We investigate the influence of blurred ways on pattern recognition of a Barabasi-Albert scale-free Hopfield neural network （SFHN） with a small amount of errors. Pattern recognition is an important function of information processing in brain. Due to heterogeneous degree of scale-free network, different blurred ways have different influences on pattern recognition with same errors. Simulation shows that among partial recognition, the larger loading ratio （the number of patterns to average degree P/ （k） ） is, the smaller the overlap of SFHN is. The influence of directed （large） way is largest and the directed （small） way is smallest while random way is intermediate between them. Under the ratio of the numbers of stored patterns to the size of the network PIN is less than O. 1 conditions, there are three families curves of the overlap corresponding to directed （small）, random and directed （large） blurred ways of patterns and these curves are not associated with the size of network and the number of patterns. This phenomenon only occurs in the SFHN. These conclusions are benefit for understanding the relation between neural network structure and brain function.

关 键 词：scale-free neural network pattern recognition blurred ways 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP273.4[自动化与计算机技术—控制科学与工程]