pred.h

基于神经网络的字符识别系统

//该文件专门保存各个环境变量和输入/输出训练对
static const int Ns=60;               //训练样本的个数（无噪声的10个、含噪声的50个）
static const int Nsext=6;             //在这里，等于6，即原纯净的样本经加噪后总的训练样本的数目扩为6倍
//一定有 Ns/Nsext等于10
static const int N_P=64;              //训练样本的维数。这里为64，即采用8×8共64点阵的字体
static const int N_Layer1=9;         //第一层所含神经元个数8/11/9
static const int N_Out=4;             //输出层所含神经元个数
static const double PI=3.1415926;
static const double error_accept=0.2; //容许误差值0.02/0.05/0.2
static const double learnspeed=0.25; //学习速度0.04/0.03/0.3
static const int N_Err=15000;           //最多迭代计算N_Err次均方误差
static const double rmobp=0.8;          //动量因子 must<1
/////////////////////////////////////////////////////////////////////////////
//P_samples存储60组训练样本，其中前10组为纯净不带噪的数据，后50组为随机加噪的数据。每组数据为64维
static double P_samples[Ns][N_P]=
						{	{0,0,1,1,1,1,1,0,
						     0,0,1,0,0,0,1,0,
							 0,0,1,0,0,0,1,0,
							 0,0,1,0,0,0,1,0,
							 0,0,1,0,0,0,1,0,
							 0,0,1,0,0,0,1,0,
							 0,0,1,0,0,0,1,0,
							 0,0,1,1,1,1,1,0},//数字 0
							{0,0,0,1,0,0,0,0,
							 0,0,0,1,0,0,0,0,
							 0,0,0,1,0,0,0,0,
							 0,0,0,1,0,0,0,0,
							 0,0,0,1,0,0,0,0,
							 0,0,0,1,0,0,0,0,
							 0,0,0,1,0,0,0,0,
							 0,0,0,1,0,0,0,0},//数字 1
							{0,0,1,1,1,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,1,1,1,1,0,0,
							 0,0,1,0,0,0,0,0,
							 0,0,1,0,0,0,0,0,
							 0,0,1,0,0,0,0,0,
							 0,0,1,1,1,1,0,0},//数字 2
							{0,0,1,1,1,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,1,1,1,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,1,1,1,1,0,0},//数字 3
							{0,1,0,0,1,0,0,0,
							 0,1,0,0,1,0,0,0,
							 0,1,0,0,1,0,0,0,
							 0,1,0,0,1,0,0,0,
							 0,1,0,0,1,0,0,0,
							 0,1,1,1,1,1,0,0,
							 0,0,0,0,1,0,0,0,
							 0,0,0,0,1,0,0,0},//数字 4
							{0,0,1,1,1,1,0,0,
							 0,0,1,0,0,0,0,0,
							 0,0,1,0,0,0,0,0,
							 0,0,1,1,1,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,1,1,1,1,0,0},//数字 5
							{0,0,1,0,0,0,0,0,
							 0,0,1,0,0,0,0,0,
							 0,0,1,0,0,0,0,0,
							 0,0,1,0,0,0,0,0,
							 0,0,1,1,1,1,0,0,
							 0,0,1,0,0,1,0,0,
							 0,0,1,0,0,1,0,0,
							 0,0,1,1,1,1,0,0},//数字 6
							{0,0,1,1,1,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,0,0,0,1,0,0},//数字 7
							{0,0,1,1,1,1,1,0,
							 0,0,1,0,0,0,1,0,
							 0,0,1,0,0,0,1,0,
							 0,0,1,1,1,1,1,0,
							 0,0,1,0,0,0,1,0,
							 0,0,1,0,0,0,1,0,
							 0,0,1,0,0,0,1,0,
							 0,0,1,1,1,1,1,0},//数字 8
							{0,1,1,1,1,1,0,0,
							 0,1,0,0,0,1,0,0,
							 0,1,0,0,0,1,0,0,
							 0,1,1,1,1,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,0,0,0,0,1,0,0,
							 0,1,1,1,1,1,0,0}//数字 9
						}; 
static double P_withnoise[60][N_P];
static double T_output[Ns][N_Out]=
						{   {0.01, 0.01, 0.01, 0.01},//0
							{0.01, 0.01, 0.01, 0.99},//1
							{0.01, 0.01, 0.99, 0.01},//2
							{0.01, 0.01, 0.99, 0.99},//3
							{0.01, 0.99, 0.01, 0.01},//4
							{0.01, 0.99, 0.01, 0.99},//5
							{0.01, 0.99, 0.99, 0.01},//6
							{0.01, 0.99, 0.99, 0.99},//7
							{0.99, 0.01, 0.01, 0.01},//8
							{0.99, 0.01, 0.01, 0.99} //9						
						}; //每行代表一个标准期望输出（与P_samples对应）
static double numE[N_Err];