public static double getNIEL(double x)
{
double[] iw = { -11.5772178845514, -8.88127201605640, -3.59371936318553, 3.15880004896414, 7.85575805562293 };
double[] lw = { 0.0221103684350823, 0.130030957979784, 0.470082360480904, -0.426894841764735, 5.00525322882412 };
double[] b1 = { 10.5172229042891, 4.41766107430249, 0.0947560961092802, 1.34147991249607, 9.45152940865414 };
double b2 = -4.96372881763975;
Neuron_network NIEL_network = new Neuron_network(iw, lw, b1, b2, 5, 4.0, -3.699, 0.7497, -2.9431, true, true);
return(NIEL_network.input(x));
}
static double Weibull_1(double x)
{
double[] iw = { -6.94788897658893, 7.06466860254654, 6.48566799525212, -7.16499577994326, -20.0488220313215 };
double[] lw = { 0.0564593834794398, 0.0888485956702163, 0.0569588186398885, -0.0882099550386574, -6.22203776041643 };
double[] b1 = { 7.02625749805833, -3.00547732132522, 0.934092266745334, -4.29365188842030, -20.7167007472045 };
double b2 = -5.49547459813758;
Neuron_network weibull = new Neuron_network(iw, lw, b1, b2, 5, 112.0, 1.1, -6.8327, -11.1791, false, false);
return(Math.Pow(10, weibull.input(x)));
}//LET阈值，饱和截面
public static Neuron_network ElectronLET()
{
double[] iw2 = { 4.40554824978072, -2.81064364523242, -3.03512283712394, 9.91858288567110, 2.60301088165564 };
double[] lw2 = { 11.4180773930414, -2.74604567663715, -0.0162365504655352, -0.721185700576192, -1.80969138100576 };
double[] b12 = { -6.13262944318743, 3.39853818000505, -0.415136851360895, 12.1386316411607, 3.32427909745692 };
double b22 = 15.6828753002702;
Neuron_network LET_electron = new Neuron_network(iw2, lw2, b12, b22, 5, 3.0, -2.0, 46.7, 1.53, true, false);
return(LET_electron);
}
public static Neuron_network ProtonLET()
{
double[] iw = { 6.86733640551202, 5.08313763261494, -44.3418366892818, -1.99877898702244, -4.72829769037351 };
double[] lw = { -0.0273795368754960, 0.0398208891139150, 0.00355504412093733, 2.24473564071721, -1.59960731323085 };
double[] b1 = { -4.63511389379558, -3.35984406955077, 4.47247412693705, -1.27467476368094, -4.14729630697173 };
double b2 = -0.367810983024666;
Neuron_network LET_proton = new Neuron_network(iw, lw, b1, b2, 5, 3.0, -2.0, 538, 1.805, true, false);
return(LET_proton);
}
static double getSEE(double[] e1, double[] f1, int material)
{
Neuron_network n = ProtonLET();
double result = 0;
for (int i = 0; i < e1.Length - 1; i++)
{
if (e1[i] == 0)
{
continue;
}
double[] w = { 0, 0 };
switch (material)
{
case 1:
w[0] = Weibull_1(n.input(e1[i]));
w[1] = Weibull_1(n.input(e1[i + 1]));
break;
case 2:
w[0] = Weibull_2(n.input(e1[i]));
w[1] = Weibull_2(n.input(e1[i + 1]));
break;
case 3:
w[0] = Weibull_3(n.input(e1[i]));
w[1] = Weibull_3(n.input(e1[i + 1]));
break;
case 4:
w[0] = Weibull_4(n.input(e1[i]));
w[1] = Weibull_4(n.input(e1[i + 1]));
break;
}
result += (f1[i] * w[0] + f1[i + 1] * w[1]) * Math.Abs(n.input(e1[i + 1]) - n.input(e1[i])) / 2;
}
return(result * 6e6);
}
