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The function `gensim`

generates
block descriptions of networks so you can simulate them using Simulink^{®} software.

gensim(net,st)

The second argument to `gensim`

determines
the sample time, which is normally chosen to be some positive real
value.

If a network has no delays associated with its input weights
or layer weights, this value can be set to -1. A value of -1 causes `gensim`

to
generate a network with continuous sampling.

Here is a simple problem defining a set of inputs `p`

and
corresponding targets `t`

.

p = [1 2 3 4 5]; t = [1 3 5 7 9];

The code below designs a linear layer to solve this problem.

net = newlind(p,t)

You can test the network on your original inputs with `sim`

.

y = sim(net,p)

The results show the network has solved the problem.

y = 1.0000 3.0000 5.0000 7.0000 9.0000

Call `gensim`

as follows to generate a Simulink version
of the network.

gensim(net,-1)

The second argument is -1, so the resulting network block samples continuously.

The call to `gensim`

opens the following Simulink Editor,
showing a system consisting of the linear network connected to a sample
input and a scope.

To test the network, double-click the input Constant `x1`

block
on the left.

The input block is actually a standard Constant block. Change
the constant value from the initial randomly generated value to `2`

,
and then click **OK**.

Select the menu option **Simulation** > **Run**. Simulink takes a
moment to simulate the system.

When the simulation is complete, double-click the output `y1`

block
on the right to see the following display of the network's
response.

Note that the output is 3, which is the correct output for an input of 2.

Here are a couple exercises you can try.

Replace the constant input block with a signal generator from the standard Simulink Sources blockset. Simulate the system and view the network's response.

Recreate the network, but with a discrete sample time of 0.5, instead of continuous sampling.

gensim(net,0.5)

Again, replace the constant input with a signal generator. Simulate the system and view the network's response.

For information on simulating and deploying neural networks
with MATLAB^{®} functions, see Deploy Trained Neural Network Functions.

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