exampleindex.m

数字识别基于MATLAB的数字识别系统的研究与实现

clc;
clear;
close;
%BP网络实验
Hbp3=figure('color',[0.8 0.8 0.8],'position',[120 120 600 400],'name',...
   '数字识别','numbertitle','off');

%界面说明
uicontrol(Hbp3,'style','text','units','normalized','position',[0.1 0.8 0.8 0.15],...
   'horizontal','center','string','试用BP网络进行0～9十个数字字符的识别','back',[0.8 0.8 0.8],...
   'fontsize',12,'fontWeight','bold');

%框架
%uicontrol(Hbp3,'style','frame','units','normalized','position',[0.08 0.08 0.37 0.78],...
 %  'back',[0.8 0.8 0.8]);

uicontrol(Hbp3,'style','text','units','normalized','position',[0.12 0.75 0.32 0.1],...
   'string','待识别数字：','back',[0.8 0.8 0.8],'horizontal','left','fontsize',12);
%uicontrol(Hbp3,'style','frame','units','normalized','position',[0.12 0.5 0.28 0.27],...
 %  'back',[0.8 0.8 0.8]);
Hbp3_axes=axes('position',[0.12 0.5 0.28 0.27]);


uicontrol(Hbp3,'style','text','units','normalized','position',[0.12 0.33 0.32 0.1],...
  'string','  识别结果：','back',[0.8 0.8 0.8],'horizontal','left','fontsize',12);
uicontrol(Hbp3,'style','frame','units','normalized','position',[0.12 0.11 0.28 0.27],...
   'back',[0.8 0.8 0.8]);
Hbp3_recog=uicontrol(Hbp3,'style','text','units','normalized','position',[0.121 0.112 0.278 0.266],...
   'back',[0.8 0.8 0.8]);

%网络参数设置
uicontrol(Hbp3,'style','text','units','normalized','position',[0.54 0.7 0.3 0.1],...
   'string','网络参数设置','back',[0.8 0.8 0.8],'horizontal','left','fontsize',12);

uicontrol(Hbp3,'style','text','units','normalized','position',[0.54 0.62 0.2 0.1],...
   'string','最大训练步数：','back',[0.8 0.8 0.8],'horizontal','left','fontsize',12);
Hbp3_step=uicontrol(Hbp3,'style','edit','units','normalized','position',[0.54 0.62 0.13 0.05],...
   'string','2000','back',[0.8 0.8 0.8],'horizontal','left','fontsize',12);

uicontrol(Hbp3,'style','text','units','normalized','position',[0.54 0.50 0.2 0.1],...
   'string','学习速率：','back',[0.8 0.8 0.8],'horizontal','left','fontsize',12);
Hbp3_learn=uicontrol(Hbp3,'style','edit','units','normalized','position',[0.54 0.50 0.13 0.05],...
   'string','0.0005','back',[0.8 0.8 0.8],'horizontal','left','fontsize',12);

uicontrol(Hbp3,'style','text','units','normalized','position',[0.54 0.37 0.2 0.1],...
   'string','期望误差：','back',[0.8 0.8 0.8],'horizontal','left','fontsize',12);
Hbp3_error=uicontrol(Hbp3,'style','edit','units','normalized','position',[0.54 0.37 0.13 0.05],...
   'string','0.02','back',[0.8 0.8 0.8],'horizontal','left','fontsize',12);

uicontrol(Hbp3,'style','text','units','normalized','position',[0.54 0.24 0.2 0.1],...
   'string','隐层神经元数：','back',[0.8 0.8 0.8],'horizontal','left','fontsize',12 );Hbp3_num=uicontrol(Hbp3,'style','edit','units','normalized','position',[0.54 0.24 0.13 0.05],...
   'string','16','back',[0.8 0.8 0.8],'horizontal','left','fontsize',12);



%按钮
uicontrol(Hbp3,'style','push','units','normalized','position',[0.78 0.75 0.16 0.08],...
   'string','训练网络','fontsize',12,'callback',[...
                                              'S1=get(Hbp3_num,''string'');'...
                                              'max_epoch=get(Hbp3_step,''string'');'...
                                              'err_goal=get(Hbp3_error,''string'');'...
                                              'lr=get(Hbp3_learn,''string'');'...
                                              'exampleTr']);
                                      
uicontrol(Hbp3,'style','push','units','normalized','position',[0.78 0.65 0.16 0.08],...
   'string','待识别图像','fontsize',12,'callback', 'example_figure;');                             
                                                                                       

uicontrol(Hbp3,'style','push','units','normalized','position',[0.78 0.55 0.16 0.08],...
   'string','显示识别','fontsize',12,'callback', 'exampleRe');                     
                                                                                 
uicontrol(Hbp3,'style','push','units','normalized','position',[0.78 0.45 0.16 0.08],...
   'string','清除显示','callback','example_clear','fontsize',12);