tcnn.cpp

一个用于JSP神经网络的演示软件

#include <time.h>
#include <stdlib.h>
#include <math.h>
#include "TCNN.h"

TCNN::TCNN(int n, int m, int** machine/* = NULL */, int** protime/* = NULL */, int mcost/* = -1 */)
:HNN(n, m, machine, protime), mincost(mcost)
{
	srand(time(0));
}



void TCNN::setTCPara(double k, double a, double b, double I0, double z0)
{
	this->k = k;
	this->a = a;
	this->b = b;
	this->I0 = I0;
	this->z0 = z0;
}


bool TCNN::run()
{
	//初始化神经网络
	cout.close();
	cout.open("result.txt");
	cout<<"Initial state:"<<endl;
	
	initI();
	printI();

	initNeros();
	printNeroInputs();
	printNeroOutputs();

	z = z0;

	cout<<"\n\nStart to run..."<<endl;
	
	int num = 0;
	//运行神经网络
	double starttime = GetTickCount();
	for(int t = 0; t <1000; t++)
	{
		cout<<"\nTime "<<t+1<<":"<<endl;
		//解神经元运动方程: u(t+1) = k*u(t)+a*M-z(t)(v(t)-I0)
		solveMotionEqu();
		printNeroInputs();
		
		//计算神经元输出
		neroOutputs();	
		printNeroOutputs();

		//判断神经网络是否已经收敛	
		double pE = E;	
		countE();
		cout<<"E: "<<E<<endl;
		if (fabs(E-pE)<0.00001) num++;
		else num = 0;
		if (num > 15 && fabs(E)<0.00001)	break;
	}
	double endtime = GetTickCount();
	double runtime = (endtime - starttime)/1000.0;
	formatTime(runtime);
	
	//处理所得调度结果
	chart->setMatrix(v);
	chart->constructGantt();
	cost = chart->getMaxTime();

	return chart->isValid();

}


void TCNN::solveMotionEqu()
{
	//累计w*v,得神经运动方程项M
	countM();
	
	//计算自反馈连接权z
	z = z*(1-b);

	//解基于退火策略的混沌动力学方程
	for(int i = 0; i < N; i++)
	{
		for(int j = 0; j < N+1; j++)
		{
			u[i][j] = k*u[i][j]+a*M[i][j]-z*(v[i][j]-I0);
		}
	}

}