/*--------------------------------------------------------------------------
Author: Thomas Nowotny
Institute: Institute for Nonlinear Dynamics
University of California San Diego
La Jolla, CA 92093-0402
email to: tnowotny@ucsd.edu
initial version: 2005-08-17
--------------------------------------------------------------------------*/
#ifndef CN_RK6N_NOISE_CC
#define CN_RK6N_NOISE_CC
#include "CN_rk6n_noise.h"
#include <iostream>
rk6n_noise::rk6n_noise(int dim)
{
N= dim;
for (int i= 0; i < 9; i++) {
Y[i]= new double[N];
F[i]= new double[N];
NS[i]= new double[N];
for (int j= i; j < 8; j++) {
a[i][j]= 0.0;
}
}
a[1][0]= 0.111111111111111;
a[2][0]= 0.555555555555556e-1;
a[2][1]= 0.555555555555556e-1;
a[3][0]= 0.416666666666667e-1;
a[3][1]= 0.0;
a[3][2]= 0.125;
a[4][0]= 0.166666666666667;
a[4][1]= 0.0;
a[4][2]= -0.5;
a[4][3]= 0.666666666666667;
a[5][0]= 0.1875e+1;
a[5][1]= 0.0;
a[5][2]= -0.7875e+1;
a[5][3]= 0.7e+1;
a[5][4]= -0.5;
a[6][0]= -0.4227272727272727e+1;
a[6][1]= 0.0;
a[6][2]= 0.176995738636364e+2;
a[6][3]= -0.142883522727273e+2;
a[6][4]= 0.522017045454545;
a[6][5]= 0.104403409090909e+1;
a[7][0]= 0.840622673179752e+1;
a[7][1]= 0.0;
a[7][2]= -0.337303717185049e+2;
a[7][3]= 0.271460231129622e+2;
a[7][4]= 0.342046929709216;
a[7][5]= -0.184653767923258e+1;
a[7][6]= 0.577349465373733;
a[8][0]= 0.128104575163399;
a[8][1]= 0.0;
a[8][2]= 0.0;
a[8][3]= -0.108433734939759;
a[8][4]= 0.669375;
a[8][5]= -0.146666666666667;
a[8][6]= 0.284444444444444;
a[8][7]= 0.173176381998583;
b[0]= 0.567119155354449e-1;
b[1]= 0.0;
b[2]= 0.0;
b[3]= 0.210909572355356;
b[4]= 0.141490384615385;
b[5]= 0.202051282051282;
b[6]= 0.253186813186813;
b[7]= 0.843679809736684e-1;
b[8]= 0.512820512820513e-1;
}
rk6n_noise::~rk6n_noise()
{
for (int i= 0; i < 9; i++) {
delete[] Y[i];
delete[] F[i];
delete[] NS[i];
}
}
void rk6n_noise::integrate(double *y, double *yn,
NeuronModel *model, double dt)
{
sqrtdt= sqrt(dt);
// calculate iterative terms rk6_Y[__i] and rk6_F[__i] (to sixth order)
// yn[0]= y[0]+dt;
for (k= 0; k < N; k++) {
Y[0][k]= y[k];
}
model->derivative(Y[0], F[0]);
model->noise(Y[0], NS[0]);
for (i= 1; i < 9; i++)
{
for (k= 0; k < N; k++)
{
aF= 0.0;
aNS= 0.0;
for (j= 0; j < i; j++) {
aF+= a[i][j]*F[j][k];
aNS+= a[i][j]*NS[j][k];
}
Y[i][k]= y[k]+dt*aF+sqrtdt*aNS;
}
model->derivative(Y[i], F[i]);
model->noise(Y[i], NS[i]);
}
// no build of 5th order scheme
// sum up rk6_Y[__i] and rk6_F[__i] to build 6th order scheme -> yn
for (k= 0; k < N; k++)
{
aF= 0.0;
aNS= 0.0;
for (j= 0; j < 9; j++) {
aF+= b[j]*F[j][k];
aNS+= b[j]*NS[j][k];
}
yn[k]= y[k]+ dt*aF + sqrtdt*aNS;
}
}
#endif
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