矩阵乘法返回错误的值

问题描述

Hello All，

我正在使用MATLAB训练的ANN中的权重和偏差来计算值。试图对S形模拟方程进行编码，但由于某种原因，C＃计算的变化比MATLAB的变化太大。即错误太高。我试图检查等式的每一步，找出造成问题的具体部分（强调部分），但我不知道如何解决这个问题，如果有人可以提供帮助，那将是一个巨大的帮助。 />


1+（purelin（net.LW {2}×（tansig（net.IW {1}×（1-（abs）（2×（[输入] -1） ）））））+ net.b {1}））+ net.b {2}））/ 2



我尝试了什么：

 //数据规范化
 public double规范化（double x，double xMAx，double xMin）
 {
 double xNorm = 0.0; 
 xNorm =（x  -  xMin）/（xMAx  -  xMin）; 
 if（xNorm< 0）
 xNorm = 0; 
 
 if（xNorm> 1）
 xNorm = 1; 
 xNorm = Math.Round（xNorm，4）; 
返回xNorm; 
} 
 
 //计算基于ANN的输出值的公式
 public double MetrixCalc（double [] Pn，double [，] W1，double [] W2，double [] b1 ，double b2，double maxValue，double minValue）
 {
 
 double FinalValue = 0; 
 
 double [] PnCalc1 = new double [Pn.Length]; 
 double [] PnCalc2 = new double [W1.Length / Pn.Length]; 
 
 for（int i = 0; i< Pn.Length; i ++）
 {
 PnCalc1 [i] = 1  -  Math.Abs​​（2 *（Pn [i ]  -  1））; 
} 
 
 for（int i = 0; i<（W1.Length / Pn.Length）; i ++）
 {
 double PnCalc = 0.0; 
 for（int j = 0; j< Pn.Length; j ++）
 {
 PnCalc = PnCalc +（W1 [i，j] * PnCalc1 [j]）; 
} 
 PnCalc2 [i] = PnCalc; 
} 
 
 for（int i = 0; i< PnCalc2.Length; i ++）
 {
 // PnCalc2 [i] = Math.Tanh（PnCalc2 [i] + b1 [i]）; 
 PnCalc2 [i] = PnCalc2 [i] + b1 [i]; 
 PnCalc2 [i] = 2.0 /（1 + Math.Exp（-2 *（PnCalc2 [i]））） -  1; 
 PnCalc2 [i] = Math.Round（PnCalc2 [i]，4）; 
} 
 
 double FinalCalc = 0.0; 
 
 for（int i = 0; i< PnCalc2.Length; i ++）
 {
 * FinalCalc = FinalCalc +（W2 [i] *（PnCalc2 [i]） ）; * 
 // FinalValue = FinalCalc; 
} 
 
 FinalValue = FinalCalc + b2; 
 FinalValue = 1 + FinalValue; 
 FinalValue =（1 + FinalValue）/ 2.0; 
 FinalValue =（FinalValue *（maxValue  -  minValue））+ minValue; 
 FinalValue = Math.Round（FinalValue，4）; 
 FinalValue = Math.Abs​​（FinalValue）; 
 
返回FinalValue; 
} 

解决方案

Hello All,
I am calculating values by using weights and bias from MATLAB trained ANN. trying to code a sigmoid simulation equation, but for some reason C# calculations vary too much than that of MATLAB. i.e. error is too high. I tried to check each step of the equation and found out the specific part that is creating the problem (Emphasized part), but I don't know how to solve this issue, if someone could help, would be a huge favour.

1+(purelin(net.LW{2}×(tansig(net.IW{1}×(1-(abs(2×([inputs]-1)))))+net.b{1}))+net.b{2}))/2

What I have tried:

//Normalization of Data
        public double Normalization(double x, double xMAx, double xMin)
        {
                double xNorm = 0.0;
                xNorm = (x - xMin) / (xMAx - xMin);
            if (xNorm < 0)
                xNorm = 0;

            if (xNorm > 1)
                xNorm = 1;
        xNorm = Math.Round(xNorm, 4);
        return xNorm;
    }

        // Equation to calculate ANN based Output Values
        public double MetrixCalc(double[] Pn, double[,] W1, double[] W2, double[] b1, double b2, double maxValue, double minValue)
        {

            double FinalValue = 0;

            double[] PnCalc1 = new double[Pn.Length];
            double[] PnCalc2 = new double[W1.Length / Pn.Length];

            for (int i = 0; i < Pn.Length; i++)
            {
                PnCalc1[i] = 1 - Math.Abs(2 * (Pn[i] - 1));
            }

        for (int i = 0; i < (W1.Length / Pn.Length); i++)
        {
            double PnCalc = 0.0;
            for (int j = 0; j < Pn.Length; j++)
            {
                PnCalc = PnCalc + (W1[i, j] * PnCalc1[j]);
            }
            PnCalc2[i] = PnCalc;
        }

        for (int i = 0; i < PnCalc2.Length; i++)
        {
            //PnCalc2[i] = Math.Tanh(PnCalc2[i] + b1[i]);
            PnCalc2[i] = PnCalc2[i] + b1[i];
            PnCalc2[i] = 2.0 / (1 + Math.Exp(-2 * (PnCalc2[i]))) - 1;
            PnCalc2[i] = Math.Round(PnCalc2[i], 4);
        }

        double FinalCalc = 0.0;

        for (int i = 0; i < PnCalc2.Length; i++)
        {
            *FinalCalc = FinalCalc + (W2[i] * (PnCalc2[i]));*
            //FinalValue = FinalCalc;
        }

        FinalValue = FinalCalc + b2;
        FinalValue = 1 + FinalValue;
        FinalValue = (1 + FinalValue) / 2.0;
        FinalValue = (FinalValue * (maxValue - minValue)) + minValue;
        FinalValue = Math.Round(FinalValue, 4);
        FinalValue = Math.Abs(FinalValue);

        return FinalValue;
        }

解决方案

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