## Deploy Shallow Neural Network Simulink Diagrams

The `gensim` function generates block descriptions of networks that you can simulate using Simulink®.

```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, you can set this value to -1. A value of -1 causes `gensim` to generate a network with continuous sampling.

### Example

Define a set of inputs `p` and the corresponding targets `t`.

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

Design a linear layer to solve this problem.

```net = newlind(p,t) ```

Test the network on your original inputs with `sim`.

```y = sim(net,p) ```

The results show that the network has solved the problem.

```y = 1.0000 3.0000 5.0000 7.0000 9.0000 ```

Call `gensim` 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 this model in the Simulink Editor, that shows 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 .

Select the menu option Simulation > Run.

When the simulation is complete, double-click the output `y1` block on the right to see the network response.

The output is equal to 3, which is the correct output for an input of 2.

### Suggested Exercises

Here are a couple exercises you can try.

#### Change the Input Signal

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

#### Use a Discrete Sample Time

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 response.

### Generate Functions and Objects

For information on simulating and deploying shallow neural networks with MATLAB® functions, see Deploy Shallow Neural Network Functions.