# Documentation

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## Generalized Regression Neural Networks

### Network Architecture

A generalized regression neural network (GRNN) is often used for function approximation. It has a radial basis layer and a special linear layer.

The architecture for the GRNN is shown below. It is similar to the radial basis network, but has a slightly different second layer.

Here the nprod box shown above (code function `normprod`) produces S2 elements in vector n2. Each element is the dot product of a row of LW2,1 and the input vector a1, all normalized by the sum of the elements of a1. For instance, suppose that

```LW{2,1}= [1 -2;3 4;5 6]; a{1} = [0.7;0.3]; ```

Then

```aout = normprod(LW{2,1},a{1}) aout = 0.1000 3.3000 5.3000 ```

The first layer is just like that for `newrbe` networks. It has as many neurons as there are input/ target vectors in P. Specifically, the first-layer weights are set to P`'`. The bias b1 is set to a column vector of 0.8326/`SPREAD`. The user chooses `SPREAD`, the distance an input vector must be from a neuron's weight vector to be 0.5.

Again, the first layer operates just like the `newrbe` radial basis layer described previously. Each neuron's weighted input is the distance between the input vector and its weight vector, calculated with `dist`. Each neuron's net input is the product of its weighted input with its bias, calculated with `netprod`. Each neuron's output is its net input passed through `radbas`. If a neuron's weight vector is equal to the input vector (transposed), its weighted input will be 0, its net input will be 0, and its output will be 1. If a neuron's weight vector is a distance of `spread` from the input vector, its weighted input will be `spread`, and its net input will be sqrt(−log(.5)) (or 0.8326). Therefore its output will be 0.5.

The second layer also has as many neurons as input/target vectors, but here `LW{2,1}` is set to `T`.

Suppose you have an input vector p close to pi, one of the input vectors among the input vector/target pairs used in designing layer 1 weights. This input p produces a layer 1 ai output close to 1. This leads to a layer 2 output close to ti, one of the targets used to form layer 2 weights.

A larger `spread` leads to a large area around the input vector where layer 1 neurons will respond with significant outputs. Therefore if `spread` is small the radial basis function is very steep, so that the neuron with the weight vector closest to the input will have a much larger output than other neurons. The network tends to respond with the target vector associated with the nearest design input vector.

As `spread` becomes larger the radial basis function's slope becomes smoother and several neurons can respond to an input vector. The network then acts as if it is taking a weighted average between target vectors whose design input vectors are closest to the new input vector. As `spread` becomes larger more and more neurons contribute to the average, with the result that the network function becomes smoother.

### Design (newgrnn)

You can use the function `newgrnn` to create a GRNN. For instance, suppose that three input and three target vectors are defined as

```P = [4 5 6]; T = [1.5 3.6 6.7]; ```

You can now obtain a GRNN with

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

and simulate it with

```P = 4.5; v = sim(net,P); ```

You might want to try `demogrn1`. It shows how to approximate a function with a GRNN.

Function

Description

`compet`

Competitive transfer function.

`dist`

Euclidean distance weight function.

`dotprod`

Dot product weight function.

`ind2vec`

Convert indices to vectors.

`negdist`

Negative Euclidean distance weight function.

`netprod`

Product net input function.

`newgrnn`

Design a generalized regression neural network.

`newpnn`

Design a probabilistic neural network.

`newrb`

Design a radial basis network.

`newrbe`

Design an exact radial basis network.

`normprod`

Normalized dot product weight function.

`radbas`

Radial basis transfer function.

`vec2ind`

Convert vectors to indices.