C++ Neural Networks and Fuzzy Logic by Valluru B. Rao M&T Books, IDG Books Worldwide, Inc. ISBN: 1558515526   Pub Date: 06/01/95

Header File

Listing 9.1 fuzzyam.h

//fuzzyam.h   V. Rao, H. Rao

#include <iostream.h>
#define MXSIZ 10

class fzneuron
{

protected:
       int nnbr;
       int inn,outn;
       float output;
       float activation;
       float outwt[MXSIZ];
       char *name;
       friend class network;

public:
       fzneuron() { };
       void getnrn(int,int,int,char *);
};

class exemplar
{
protected:
       int xdim,ydim;
       float v1[MXSIZ],v2[MXSIZ];   // this is different from BAM
       friend class network;

public:
       exemplar() { };
       void getexmplr(int,int,float *,float *);
       void prexmplr();
};

class asscpair
{
protected:
       int xdim,ydim,idn;
       float v1[MXSIZ],v2[MXSIZ];
       friend class network;

public:
       asscpair() { };
       void getasscpair(int,int,int);
       void prasscpair();
};

class potlpair
{
protected:
       int xdim,ydim;
       float v1[MXSIZ],v2[MXSIZ];
       friend class network;

public:
       potlpair() { };
       void getpotlpair(int,int);
       void prpotlpair();
};

class network
{
public:
       int  anmbr,bnmbr,flag,nexmplr,nasspr,ninpt;
       fzneuron (anrn)[MXSIZ],(bnrn)[MXSIZ];
       exemplar (e)[MXSIZ];
       asscpair (as)[MXSIZ];
       potlpair (pp)[MXSIZ];
       float outs1[MXSIZ],outs2[MXSIZ];   // change from BAM to floats
       double mtrx1[MXSIZ][MXSIZ],mtrx2[MXSIZ][MXSIZ]; // change from
BAM to doubles

       network() { };
       void getnwk(int,int,int,float [][6],float [][4]);
       void compr1(int,int);
       void compr2(int,int);
       void prwts();
       void iterate();
       void findassc(float *);
       void asgninpt(float *);
       void asgnvect(int,float *,float *);
       void comput1();
       void comput2();
       void prstatus();
};

Source File

Listing 9.2 fuzzyam.cpp

//fuzzyam.cpp   V. Rao, H. Rao

#include "fuzzyam.h"

float max(float x,float y)   //new for FAM
{
float u;
u = ((x>y) ? x : y );
return u;
}

float min(float x,float y)      // new for FAM
{
float u;
u =( (x>y) ? y : x) ;
return u;
}

void fzneuron::getnrn(int m1,int m2,int m3,char *y)
{
int i;
name = y;
nnbr = m1;
outn = m2;
inn  = m3;

for(i=0;i<outn;++i){
       outwt[i] = 0 ;
       }

output = 0;
activation = 0;
}

void exemplar::getexmplr(int k,int l,float *b1,float *b2)    // changed
from BAM
{
int i2;
xdim = k;
ydim = l;

for(i2=0;i2<xdim;++i2){
       v1[i2] = b1[i2]; }

for(i2=0;i2<ydim;++i2){
       v2[i2] = b2[i2]; }

}

void exemplar::prexmplr()
{
int i;
cout<<"\nX vector you gave is:\n";

for(i=0;i<xdim;++i){
       cout<<v1[i]<<"  ";}
cout<<"\nY vector you gave is:\n";

for(i=0;i<ydim;++i){
       cout<<v2[i]<<"  ";}

cout<<"\n";
}

void asscpair::getasscpair(int i,int j,int k)
{
idn = i;
xdim = j;
ydim = k;
}

void asscpair::prasscpair()
{
int i;
cout<<"\nX vector in the associated pair no. "<<idn<<"is:\n";

for(i=0;i<xdim;++i){
       cout<<v1[i]<<"  ";}

cout<<"\nY vector in the associated pair no. "<<idn<<"is:\n";

for(i=0;i<ydim;++i){
       cout<<v2[i]<<"  ";}

cout<<"\n";
}

void potlpair::getpotlpair(int k,int j)
{
xdim = k;
ydim = j;

}

void potlpair::prpotlpair()
{
int i;
cout<<"\nX vector in possible associated pair is:\n";

for(i=0;i<xdim;++i){
       cout<<v1[i]<<"  ";}

cout<<"\nY vector in possible associated pair is:\n";

for(i=0;i<ydim;++i){
       cout<<v2[i]<<"  ";}

cout<<"\n";
}

void network::getnwk(int k,int l,int k1,float b1[][6],float
       b2[][4])
{
anmbr = k;
bnmbr = l;
nexmplr = k1;
nasspr = 0;
ninpt = 0;
int i,j,i2;
float tmp1,tmp2;
flag =0;
char *y1="ANEURON", *y2="BNEURON" ;

for(i=0;i<nexmplr;++i){
       e[i].getexmplr(anmbr,bnmbr,b1[i],b2[i]);
       e[i].prexmplr();
       cout<<"\n";
       }

for(i=0;i<anmbr;++i){
       anrn[i].fzneuron::getnrn(i,bnmbr,0,y1);}

for(i=0;i<bnmbr;++i){
       bnrn[i].fzneuron::getnrn(i,0,anmbr,y2);}

for(i=0;i<anmbr;++i){

       for(j=0;j<bnmbr;++j){
              tmp1 = 0.0;

              for(i2=0;i2<nexmplr;++i2){
                      tmp2 = min(e[i2].v1[i],e[i2].v2[j]);
                      tmp1 = max(tmp1,tmp2);
              }

              mtrx1[i][j] = tmp1;
              mtrx2[j][i] = mtrx1[i][j];
              anrn[i].outwt[j] = mtrx1[i][j];
              bnrn[j].outwt[i] = mtrx2[j][i];
              }

       }

prwts();
cout<<"\n";
}

void network::asgninpt(float *b)
{
int i,j;
cout<<"\n";

for(i=0;i<anmbr;++i){
       anrn[i].output = b[i];
       outs1[i] = b[i];
       }

}

void network::compr1(int j,int k)
{
int i;

for(i=0;i<anmbr;++i){

       if(pp[j].v1[i] != pp[k].v1[i]) flag = 1;

       break;
       }
}

void network::compr2(int j,int k)
{
int i;

for(i=0;i<anmbr;++i){

       if(pp[j].v2[i] != pp[k].v2[i]) flag = 1;

       break;}

}

void network::comput1()   //changed from BAM
{
int j;

for(j=0;j<bnmbr;++j){
       int ii1;
       float c1 =0.0,d1;
       cout<<"\n";

       for(ii1=0;ii1<anmbr;++ii1){
              d1 = min(outs1[ii1],mtrx1[ii1][j]);
              c1 = max(c1,d1);
              }

       bnrn[j].activation = c1;
       cout<<"\n output layer neuron  "<<j<<" activation is "
              <<c1<<"\n";
       bnrn[j].output = bnrn[j].activation;
       outs2[j] = bnrn[j].output;
       cout<<"\n output layer neuron  "<<j<<" output is "
              <<bnrn[j].output<<"\n";
       }
}

void network::comput2()             //changed from BAM
{
int i;

for(i=0;i<anmbr;++i){
       int ii1;
       float c1=0.0,d1;

       for(ii1=0;ii1<bnmbr;++ii1){
              d1 = min(outs2[ii1],mtrx2[ii1][i]);
              c1 = max(c1,d1);}

       anrn[i].activation = c1;
       cout<<"\ninput layer neuron "<<i<<"activation is "
              <<c1<<"\n";
       anrn[i].output = anrn[i].activation;
       outs1[i] = anrn[i].output;
       cout<<"\n input layer neuron  "<<i<<"output is "
              <<anrn[i].output<<"\n";
       }

}

void network::asgnvect(int j1,float *b1,float *b2)
{
int  j2;

for(j2=0;j2<j1;++j2){
       b2[j2] = b1[j2];}

}

void network::prwts()
{
int i3,i4;
cout<<"\n  weights--  input layer to output layer: \n\n";

for(i3=0;i3<anmbr;++i3){

       for(i4=0;i4<bnmbr;++i4){
              cout<<anrn[i3].outwt[i4]<<"  ";}

       cout<<"\n"; }

cout<<"\n";

cout<<"\nweights--  output layer to input layer: \n\n";

for(i3=0;i3<bnmbr;++i3){

       for(i4=0;i4<anmbr;++i4){
              cout<<bnrn[i3].outwt[i4]<<"  ";}

       cout<<"\n";  }

cout<<"\n";
}

void network::iterate()
{
int i1;

for(i1=0;i1<nexmplr;++i1){
       findassc(e[i1].v1);
       }

}

void network::findassc(float *b)
{
int j;
flag = 0;
asgninpt(b);
       ninpt ++;
       cout<<"\nInput vector is:\n" ;

       for(j=0;j<6;++j){
              cout<<b[j]<<" ";};

       cout<<"\n";
       pp[0].getpotlpair(anmbr,bnmbr);

asgnvect(anmbr,outs1,pp[0].v1);
comput1();

if(flag>=0){

       asgnvect(bnmbr,outs2,pp[0].v2);

       cout<<"\n";
       pp[0].prpotlpair();
       cout<<"\n";

       comput2(); }

for(j=1;j<MXSIZ;++j){
       pp[j].getpotlpair(anmbr,bnmbr);
       asgnvect(anmbr,outs1,pp[j].v1);
       comput1();

       asgnvect(bnmbr,outs2,pp[j].v2);

       pp[j].prpotlpair();
       cout<<"\n";

       compr1(j,j-1);
       compr2(j,j-1);

       if(flag == 0) {

              int j2;
              nasspr += 1;
              j2 = nasspr;
              as[j2].getasscpair(j2,anmbr,bnmbr);
              asgnvect(anmbr,pp[j].v1,as[j2].v1);
              asgnvect(bnmbr,pp[j].v2,as[j2].v2);

              cout<<"\nPATTERNS ASSOCIATED:\n";
              as[j2].prasscpair();
              j = MXSIZ ;
              }

       else

              if(flag == 1)
                     {
                     flag = 0;
                     comput1();
                     }

              }

}

void network::prstatus()
{
int j;
cout<<"\nTHE FOLLOWING ASSOCIATED PAIRS WERE FOUND BY FUZZY AM\n\n";

for(j=1;j<=nasspr;++j){
       as[j].prasscpair();
       cout<<"\n";}

}

void main()
{
int ar = 6, br = 4, nex = 1;
float inptv[][6]={0.1,0.3,0.2,0.0,0.7,0.5,0.6,0.0,0.3,0.4,0.1,0.2};
float outv[][4]={0.4,0.2,0.1,0.0};
cout<<"\n\nTHIS PROGRAM IS FOR A FUZZY ASSOCIATIVE MEMORY NETWORK. THE
NETWORK \n";
cout<<"IS SET UP FOR ILLUSTRATION WITH "<<ar<<" INPUT NEURONS, AND "<<br;
cout<<" OUTPUT NEURONS.\n"<<nex<<" exemplars are used to encode \n";

static network famn;
famn.getnwk(ar,br,nex,inptv,outv);
famn.iterate();
famn.findassc(inptv[1]);
famn.prstatus();

}