system identification using neural networks for the MISO system NARXNET

I need to find the mathematical model for a MISO system with data collected and be able to control the plant, take some examples of neural networks and manual I have the following:

load datoshistoricos.txt;

datoshistoricos;

u=datoshistoricos(:,1:2:3:4)';

y=datoshistoricos(:,5)';

[u,us] = mapminmax(u);

[y,ys] = mapminmax(y);

y = con2seq(y);

u = con2seq(u,587);

d1 = [1:2];

d2 = [1:2];

S1 = 20;

narx_net = narxnet(d1,d2,S1);

narx_net.divideFcn = '';

narx_net.inputs{1}.processFcns = {};

narx_net.inputs{2}.processFcns = {};

narx_net.outputs{2}.processFcns = {};

narx_net.trainParam.min_grad = 1e-10;

[p,Pi,Ai,t] = preparets(narx_net,u,{},y);

narx_net = train(narx_net,p,t,Pi);

narx_net_closed = closeloop(narx_net);

view(narx_net_closed)

mrac_net = feedforwardnet([S1 1 S1]);

mrac_net.layerConnect = [0 1 0 1;1 0 0 0;0 1 0 1;0 0 1 0];

mrac_net.outputs{4}.feedbackMode = 'closed';

mrac_net.layers{2}.transferFcn = 'purelin';

mrac_net.layerWeights{3,4}.delays = 1:2;

mrac_net.layerWeights{3,2}.delays = 1:2;

mrac_net.layerWeights{3,2}.learn = 0;

mrac_net.layerWeights{3,4}.learn = 0;

mrac_net.layerWeights{4,3}.learn = 0;

mrac_net.biases{3}.learn = 0;

mrac_net.biases{4}.learn = 0;

mrac_net.divideFcn = '';

mrac_net.inputs{1}.processFcns = {};

mrac_net.outputs{4}.processFcns = {};

mrac_net.name = 'Model Reference Adaptive Control Network';

mrac_net.layerWeights{1,2}.delays = 1:2;

mrac_net.layerWeights{1,4}.delays = 1:2;

mrac_net.inputWeights{1}.delays = 1:2;

some collaboration thanks.