%0 Journal Article
%T Estimation of small-world neuronal network topology based on relevance vector machine<br>基于相关向量机的小世界神经元网络拓扑估计
%A HAO Chong-qing
%A WANG Jiang
%A DENG Bin
%A WEI Xi-le
%A <br>郝崇清
%A 王　江
%A 邓　斌
%A 魏熙乐
%J 计算机应用研究
%D 2012
%I 
%X This paper applied relevance vector machine to estimate node dynamical equations and topology of small-world neuronal network from noisy time series. According to the fact that many dynamical equations or its power series expansion had polynomial structure, by constructing a unified polynomial and making the original dynamical equation sparse in the unified polynomial, obtained dynamical equations and network topology obtained while used sparse Bayesian learning to estimate the sparse nonzero terms. FHN small-world neuronal network as a paradigm demonstrated the estimation effect of the dynamical equations and network topology. The results show that the estimating strategy can identify equations structure and network topology accurately and quickly, the error is small in dynamical equations coefficients and couple strength estimation and is robust to noise.
%K small-world networks
%K relevance vector machine
%K dynamical equations reconstruction
%K topology estimation
%K neuron model<br>小世界网络
%K 相关向量机
%K 动力学方程重建
%K 拓扑估计
%K 神经元模型
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=3337477751B1F6026CB26CB71F9BB4B6&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=708DD6B15D2464E8&sid=0CD7E017477D6CEA&eid=518F553B720EC1A8&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=10