Linear System Design (newlind) :: Linear Filters (Neural Network Toolbox)

Neural Network Toolbox

Linear System Design (newlind)

Unlike most other network architectures, linear networks can be designed directly if input/target vector pairs are known. Specific network values for weights and biases can be obtained to minimize the mean square error by using the function newlind.

Suppose that the inputs and targets are

```
P = [1 2 3];
T= [2.0 4.1 5.9];
```

Now you can design a network.

```
net = newlind(P,T);
```

You can simulate the network behavior to check that the design was done properly.

Y = sim(net,P)
Y =
    2.0500    4.0000    5.9500

Note that the network outputs are quite close to the desired targets.

You might try demolin1. It shows error surfaces for a particular problem, illustrates the design and plots the designed solution.

The function newlind can also be used to design linear networks having delays in the input. Such networks are discussed later in this chapter. First, however, we need to discuss delays.

Mean Square Error Linear Networks with Delays