Implements of MATAB神经网络案例分析-离散Hopfield-分类.zip

%% 离散Hopfield的分类——高校科研能力评价 %% 清空环境变量 clear clc %% 导入数据 load class.mat %% 目标向量 T=[class_1 class_2 class_3 class_4 class_5]; %% 创建网络 net=newhop(T); %% 导入待分类样本 load sim.mat A={[sim_1 sim_2 sim_3 sim_4 sim_5]}; %% 网络仿真 Y=sim(net,{25 20},{},A); %% 结果显示 Y1=Y{20}(:,1:5) Y2=Y{20}(:,6:10) Y3=Y{20}(:,11:15) Y4=Y{20}(:,16:20) Y5=Y{20}(:,21:25) %% 绘图 result={T;A{1};Y{20}}; figure for p=1:3 for k=1:5 subplot(3,5,(p-1)*5+k) temp=result{p}(:,(k-1)*5+1:k*5); [m,n]=size(temp); for i=1:m for j=1:n if temp(i,j)>0 plot(j,m-i,'ko','MarkerFaceColor','k'); else plot(j,m-i,'ko'); end hold on end end axis([0 6 0 12]) axis off if p==1 title(['class' num2str(k)]) elseif p==2 title(['pre-sim' num2str(k)]) else title(['sim' num2str(k)]) end end end set(gcf,'position',[340 ,80,580,520]) % noisy=[1 -1 -1 -1 -1;-1 -1 -1 1 -1; % -1 1 -1 -1 -1;-1 1 -1 -1 -1; % 1 -1 -1 -1 -1;-1 -1 1 -1 -1; % -1 -1 -1 1 -1;-1 -1 -1 -1 1; % -1 1 -1 -1 -1;-1 -1 -1 1 -1; % -1 -1 1 -1 -1]; % y=sim(net,{5 10},{},{noisy}); % a=y{10}