How retrain yet trained closed loop NAR

I want to retrain a yet trained closed loop NAR (to improve performance), but how?

Below my code.

% from open loop neto to closed
    netc=closeloop(neto,Xoi,Aoi);
    [Xc,Xci,Aci,Tc]=preparets(netc,{},{},T);
    [Yc,Xcf,Acf]=netc(Xc,Xci,Aci);
    Ec=gsubtract(Tc,Yc);

      % some error metric
      NMSEc=mse(Ec)/varto;
      Ectrn=Ec(trnind);
      Ecval=Ec(valind);
      Ectst=Ec(tstind);
      NMSEctrn=mse(Ectrn)/var(totrn,1);
      NMSEcval=mse(Ecval)/var(toval,1);
      NMSEctst=mse(Ectst)/var(totst,1);
      R2ctrn = 1 - mse( Ectrn )/var(totrn,1);
      R2cval = 1 - mse( Ecval )/var(toval,1);
      R2ctst = 1 - mse( Ectst )/var(totst,1);
      R2c = 1 - mse( Ec )/varto; 

      %%Retrain NAR
      % [Xc,  Xci, Aci,Tc ] = preparets(netc,{},{},T); % I think this is not necessary
      % netc=configure(netc,Xc,Tc);                    % even this seems useless
      % netc.trainFcn='trainlm';           % if I not change algorithm training stops after 3 or 4 iterations

     % retrain using Xcf and Acf from netc output states? 
     [netcr,trcr, Ycr, Ecr, Acfr, Xcfr] = train(netc,Xc,Tc,Xcf,Acf);

      NMSEcr=mse(Ecr)/varto;
      R2cr = 1 - mse( Ecr )/varto;

Thanks!