lms_1.c

一个可以实用的LMS,NLMS等四种自适应滤波的MATLAB算法程序

#include"stdio.h"
#include"stdlib.h"
#include"math.h"

#define SampleNumber  500    //the number of the train sample
#define Mode 20              //the number of the mode net
#define PI 3.1415926
#define MU 0.001            //learning rate

main()
{
	double input[SampleNumber];//the input og the network
	double d_hopeoutput[SampleNumber]; //hope output of the network
	double output[SampleNumber];   // the real output of the network
	double weight[Mode];        // the weight of the weight
    void inital(double input[SampleNumber],double d_hopeoutput[SampleNumber],double weight[Mode]);  // inital the input,hopeoutput,and weight
    void train(double input[SampleNumber],double d_hopeoutput[SampleNumber],double output[SampleNumber],double weight[Mode]);
	inital(input,d_hopeoutput,weight);
	train(input,d_hopeoutput,output,weight);
    
}



void inital(double input[SampleNumber],double d_hopeoutput[SampleNumber],double weight[Mode])
{
	double gauss(double mean,double sigma,int *s);
	int i;
	double mean=0;
	double sigma=1.0;
	int seed=13579;
	FILE *FP=NULL;
	FILE *FW=NULL;
	for (i=0;i<Mode;i++)
		weight[i]=0.0;
	FP=fopen("hopeoutput.txt","w");
	FW=fopen("input.txt","w");
	if(FP==NULL) {
		printf("error to open the output file,press any key to exit\n");
		getch();
		exit(0);
	}
	for(i=0;i<SampleNumber;i++)
	{
		//input[i]=sqrt(2.0)*sin(2*PI*i/20.0);
		d_hopeoutput[i]=sqrt(2.0)*sin(2*PI*i/20.0);
		input[i]=d_hopeoutput[i]+gauss(mean,sigma,&seed);
		fprintf(FP,"%lf\n",d_hopeoutput[i]);
		fprintf(FW,"%lf\n",input[i]);
	}
	close(FP);
	fclose(FW);
   	//for(i=0;i<SampleNumber;i++)
	

}


double gauss(double mean,double sigma,int *s)
{
	double uniform(double a,double b,int *seed);
	int i;
	double x,y;
	x=0;
	for(i=0;i<12;i++)
		x+=uniform(0.0,1.0,s);
	x=x-6.0;
	y=mean+x*sigma;
	return(y);
}


double uniform(double a,double b,int *seed)
{
	double t;
	*seed=2045*(*seed)+1;
	*seed=*seed-(*seed/1048576)*1048576;
	t=(*seed)/1048576.0;
	t=a+(b-a)*t;
	return(t);
}


void train(double input[SampleNumber],double d_hopeoutput[SampleNumber],double output[SampleNumber],double weight[Mode])
{
	int count=0;
	int i=0;
	double flag=0,TotalError=0;
	FILE *FP=0;
	double lms(double input[SampleNumber],double d_hopeoutput[SampleNumber],double output[SampleNumber],double weight[Mode]);
	FP=fopen("error.txt","w");
    if(FP==NULL) {
		printf("error to open the error file,press any key to exit\n");
		getch();
		exit(0);
	}
	while(count<50)
	{
		TotalError=lms(input,d_hopeoutput,output,weight);
		fprintf(FP,"%lf\n",TotalError);
		count++;
	}
	fclose(FP);
	printf("%d\n",count);
	FP=fopen("weight.txt","w");
    if(FP==NULL) {
		printf("error to open the weight file,press any key to exit\n");
		getch();
		exit(0);
	}
	for(i=0;i<Mode;i++)
	{
		fprintf(FP,"%lf\n",weight[i]);
	}
	fclose(FP);
	FP=fopen("output.txt","w");
    if(FP==NULL) {
		printf("error to open the output file,press any key to exit\n");
		getch();
		exit(0);
	}
    for(i=0;i<SampleNumber;i++)
	{
		fprintf(FP,"%lf\n",output[i]);
	}
	fclose(FP);
	
}


double lms(double input[SampleNumber],double d_hopeoutput[SampleNumber],double output[SampleNumber],double weight[Mode])
{
	int i,k;
	double error=0;
	double TotalError=0;
	FILE *FP=NULL;
	FP=fopen("err.txt","w++");
	for(k=0;k<Mode;k++)
	{
		output[k]=0;
		for(i=0;i<=k;i++)
		{
			output[k]+=input[k-i]*weight[i];
		}
		error=d_hopeoutput[k]-output[k];
	    fprintf(FP,"%lf\n",error);
		for(i=0;i<=k;i++)
		{
			weight[i]+=2.0*MU*error*input[k-i];
		}
	    TotalError+=error;
	}
	for (k=Mode;k<SampleNumber;k++)
	{
		output[k]=0;
		for(i=0;i<Mode;i++)
		{
			output[k]+=input[k-i]*weight[i];
		}
       	error=d_hopeoutput[k]-output[k];
		for(i=0;i<Mode;i++)
		{
			weight[i]+=2.0*MU*error*input[k-i];
		}
	   fprintf(FP,"%lf\n",error);
	   TotalError+=error;
	}
	fclose(FP);
	return(TotalError);
}