# netsum

Sum net input function

## Syntax

`N = netsum({Z1,Z2,...,Zn},FP)info = netsum('code')`

## Description

`netsum` is a net input function. Net input functions calculate a layer's net input by combining its weighted inputs and biases.

`N = netsum({Z1,Z2,...,Zn},FP)` takes `Z1` to `Zn` and optional function parameters,

 `Zi` `S`-by-`Q` matrices in a row cell array `FP` Row cell array of function parameters (ignored)

and returns the elementwise sum of `Z1` to `Zn`.

`info = netsum('code')` returns information about this function. The following codes are supported:

`netsum('name')` returns the name of this function.

`netsum('type')` returns the type of this function.

`netsum('fpnames')` returns the names of the function parameters.

`netsum('fpdefaults')` returns default function parameter values.

`netsum('fpcheck', FP)` throws an error for illegal function parameters.

`netsum('fullderiv')` returns 0 or 1, depending on whether the derivative is `S`-by-`Q` or `N`-by-`S`-by-`Q`.

## Examples

Here `netsum` combines two sets of weighted input vectors and a bias. You must use `concur` to make `B` the same dimensions as `Z1` and `Z2`.

```z1 = [1 2 4; 3 4 1} z2 = [-1 2 2; -5 -6 1] b = [0; -1] n = netsum({z1,z2,concur(b,3)}) ```

Assign this net input function to layer `i` of a network.

```net.layers(i).netFcn = 'compet'; ```

Use `feedforwardnet` or `cascadeforwardnet` to create a standard network that uses `netsum`.