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www.eeworm.com/read/355155/10290525
txt l6.2.txt
%Hopfield网络，两个平衡点
T=[-1 -1 1;1 -1 1]'
%网络设计
net=newhop(T);
%验证
Ai=T;
[Y,Pf,Af]=sim(net,2,[],Ai);
Y
%仿真
Ai={[-0.9;-0.8;0.7]};
[Y,Pf,Af]=sim(net,{1 5},{},Ai);
Y{1}
www.eeworm.com/read/416449/6960156
m sofmsimu.m
% 此为Sofm网络仿真函数
% 根据训练好的网络模型，对预测数据进行分类识别
function retstr = SofmSimu(ModelNo,NetPara,SimuData,DataDir)
NNTWARN OFF
retstr=-1;
%%%% 输入参数赋值开始 %%%%%%%%%%%%%%%%%%%%%%%
% 方便调试程序用，程序调试时去掉这部分的注释
ModelNo
www.eeworm.com/read/396352/8112692
htm 165_1.htm
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td{font-size:9pt;line-height:140%}
b
www.eeworm.com/read/331558/12821452
asv fun_sigma.asv
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%计算优化影响因子sigma,这里可以计算无向图的Sigma，但是不能计算有向图的
%% 输入：网络G=(V,E),ni个节点，m条边; 网络内各个节点的质量（向量）
%% 输出：二维矩阵，【熵，影响因子】
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
www.eeworm.com/read/331558/12821524
m fun_sigma.m
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%计算优化影响因子sigma,这里可以计算无向图的Sigma，但是不能计算有向图的
%% 输入：网络G=(V,E),ni个节点，m条边; 网络内各个节点的质量（向量）
%% 输出：二维矩阵，【熵，影响因子】
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%