Simulation with Concurrent Inputs in a Static Network :: Neuron Model and Network Architectures (Neural Network Toolbox)

Neural Network Toolbox

Simulation with Concurrent Inputs in a Static Network

The simplest situation for simulating a network occurs when the network to be simulated is static (has no feedback or delays). In this case, we do not have to be concerned about whether or not the input vectors occur in a particular time sequence, so we can treat the inputs as concurrent. In addition, we make the problem even simpler by assuming that the network has only one input vector. Use the following network as an example.

To set up this feedforward network, we can use the following command.

```
net = newlin([1 3;1 3],1);
```

For simplicity assign the weight matrix and bias to be

and .

The commands for these assignments are

```
net.IW{1,1} = [1 2];
net.b{1} = 0;
```

Suppose that the network simulation data set consists of Q = 4 concurrent vectors:

Concurrent vectors are presented to the network as a single matrix:

```
P = [1 2 2 3; 2 1 3 1];
```

We can now simulate the network:

A = sim(net,P)
A =
     5     4     8     5

A single matrix of concurrent vectors is presented to the network and the network produces a single matrix of concurrent vectors as output. The result would be the same if there were four networks operating in parallel and each network received one of the input vectors and produced one of the outputs. The ordering of the input vectors is not important as they do not interact with each other.

Data Structures Simulation with Sequential Inputs in a Dynamic Network