www.gusucode.com > bp网络实现0~9数字识别系统matlab源码程序 > exampleindex.m
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);