63.

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

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Listing 8.2 bamntwrk.cpp

 //bamntwrk.cpp   V. Rao, H. Rao //Source file for BAM network program #include “bamntwrk.h” void bmneuron::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,int *b1,int *b2) { 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 exemplar::trnsfrm() { int i; for(i=0;i<xdim;++i){        u1[i] = 2*v1[i] -1;} for(i=0;i<ydim;++i){        u2[i] = 2*v2[i] - 1;} } void exemplar::prtrnsfrm() { int i; cout<<”\nbipolar version of X vector you gave is:\n”; for(i=0;i<xdim;++i){        cout<<u1[i]<<”  “;} cout<<”\nbipolar version of Y vector you gave is:\n”; for(i=0;i<ydim;++i){        cout<<u2[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,int b1[][6],int        b2[][5]) { anmbr = k; bnmbr = l; nexmplr = k1; nasspr = 0; ninpt = 0; int i,j,i2; 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();        e[i].trnsfrm();        e[i].prtrnsfrm();        } for(i=0;i<anmbr;++i){        anrn[i].bmneuron::getnrn(i,bnmbr,0,y1);} for(i=0;i<bnmbr;++i){        bnrn[i].bmneuron::getnrn(i,0,anmbr,y2);} for(i=0;i<anmbr;++i){        for(j=0;j<bnmbr;++j){               mtrx1[i][j]  = 0;        for(i2=0;i2<nexmplr;++i2){               mtrx1[i][j]  += e[i2].u1[i]*e[i2].u2[j];}        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(int *b) { int i; 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() { int j; for(j=0;j<bnmbr;++j){        int ii1;        int c1 =0,d1;        cout<<”\n”;        for(ii1=0;ii1<anmbr;++ii1){               d1 = outs1[ii1] * mtrx1[ii1][j];               c1 += d1;               }        bnrn[j].activation = c1;        cout<<”\n output layer neuron         “<<j<<” activation is”               <<c1<<”\n”; if(bnrn[j].activation <0) {        bnrn[j].output = 0;        outs2[j] = 0;} else        if(bnrn[j].activation>0) {                bnrn[j].output = 1;                outs2[j] = 1;}                else                {cout<<”\n A 0 is obtained, use previous output value \n”;                if(ninpt<=nexmplr){                          bnrn[j].output = e[ninpt-1].v2[j];}                else                          { bnrn[j].output = pp[0].v2[j];}                          outs2[j] = bnrn[j].output; }        cout<<”\n output layer neuron         “<<j<<” output is”                <<bnrn[j].output<<”\n”;        } } void network::comput2() { int i; for(i=0;i<anmbr;++i){        int ii1;        int c1=0;        for(ii1=0;ii1<bnmbr;++ii1){               c1 += outs2[ii1] * mtrx2[ii1][i];  }        anrn[i].activation = c1;        cout<<”\ninput layer neuron        “<<i<<”activation is “               <<c1<<”\n”;        if(anrn[i].activation <0 ){               anrn[i].output = 0;               outs1[i] = 0;}        else               if(anrn[i].activation >0 ) {                      anrn[i].output = 1;                      outs1[i] = 1;                      }               else               { cout<<”\n A 0 is obtained, use previous value if available\n”;               if(ninpt<=nexmplr){                         anrn[i].output = e[ninpt-1].v1[i];}               else                         {anrn[i].output = pp[0].v1[i];}               outs1[i] = anrn[i].output;}               cout<<”\n input layer neuron               “<<i<<” output is “                   <<anrn[i].output<<”\n”;               } } void network::asgnvect(int j1,int *b1,int *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(int *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 BAM\n\n”; for(j=1;j<=nasspr;++j){        as[j].prasscpair();        cout<<”\n”;} } void main() { int ar = 6, br = 5, nex = 3; int inptv[][6]={1,0,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,0,0,1,0,1,0,1,    1,1,1,1,1,1}; int outv[][5]={1,1,0,0,1,0,1,0,1,1,1,0,0,1,0}; cout<<”\n\nTHIS PROGRAM IS FOR A BIDIRECTIONAL ASSOCIATIVE MEMORY NETWORK.\n”; cout<<” THE NETWORK ISSET UP FOR ILLUSTRATION WITH “<<ar<<          “ INPUT NEURONS, AND “<<br; cout<<” OUTPUT NEURONS.\n”<<nex          <<” exemplars are used to encode \n”; static network bamn; bamn.getnwk(ar,br,nex,inptv,outv) ; bamn.iterate(); bamn.findassc(inptv[3]); bamn.findassc(inptv[4]); bamn.prstatus(); } 


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Copyright © IDG Books Worldwide, Inc.



C++ Neural Networks and Fuzzy Logic
C++ Neural Networks and Fuzzy Logic
ISBN: 1558515526
EAN: 2147483647
Year: 1995
Pages: 139

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