Neural network, specified as a network object.

**Example: **`net = feedforwardnet(10);`

Neural network target values, specified as a matrix or cell
array of numeric values. Network target values define the desired
outputs, and can be specified as an `N`

-by-`Q`

matrix
of `Q`

`N`

-element vectors, or an `M`

-by-`TS`

cell
array where each element is an `Ni`

-by-`Q`

matrix.
In each of these cases, `N`

or `Ni`

indicates
a vector length, `Q`

the number of samples, `M`

the
number of signals for neural networks with multiple outputs, and `TS`

is
the number of time steps for time series data. `targets`

must
have the same dimensions as `outputs`

.

The target matrix columns consist of all zeros and a single
1 in the position of the class being represented by that column vector.
When N = 1, the software uses cross entropy for binary encoding, otherwise
it uses cross entropy for 1-of-N encoding. `NaN`

values
are allowed to indicate unknown or don't-care output values. The
performance of `NaN`

target values is ignored.

**Data Types: **`double`

| `cell`

Neural network output values, specified as a matrix or cell
array of numeric values. Network output values can be specified as
an `N`

-by-`Q`

matrix of `Q`

`N`

-element
vectors, or an `M`

-by-`TS`

cell
array where each element is an `Ni`

-by-`Q`

matrix.
In each of these cases, `N`

or `Ni`

indicates
a vector length, `Q`

the number of samples, `M`

the
number of signals for neural networks with multiple outputs and `TS`

is
the number of time steps for time series data. `outputs`

must
have the same dimensions as `targets`

.

Outputs can include `NaN`

to indicate unknown
output values, presumably produced as a result of `NaN`

input
values (also representing unknown or don't-care values). The performance
of `NaN`

output values is ignored.

General case (N>=2): The columns of the output matrix represent
estimates of class membership, and should sum to 1. You can use the `softmax`

transfer
function to produce such output values. Use `patternnet`

to
create networks that are already set up to use cross-entropy performance
with a softmax output layer.

**Data Types: **`double`

| `cell`

Performance weights, specified as a vector or cell array of
numeric values. Performance weights are an optional argument defining
the importance of each performance value, associated with each target
value, using values between 0 and 1. Performance values of 0 indicate
targets to ignore, values of 1 indicate targets to be treated with
normal importance. Values between 0 and 1 allow targets to be treated
with relative importance.

Performance weights have many uses. They are helpful for classification
problems, to indicate which classifications (or misclassifications)
have relatively greater benefits (or costs). They can be useful in
time series problems where obtaining a correct output on some time
steps, such as the last time step, is more important than others.
Performance weights can also be used to encourage a neural network
to best fit samples whose targets are known most accurately, while
giving less importance to targets which are known to be less accurate.

`perfWeights`

can have the same dimensions
as `targets`

and `outputs`

. Alternately,
each dimension of the performance weights can either match the dimension
of `targets`

and `outputs`

, or be
1. For instance, if `targets`

is an `N`

-by-`Q`

matrix
defining `Q`

samples of `N`

-element
vectors, the performance weights can be `N`

-by-`Q`

indicating
a different importance for each target value, or `N`

-by-`1`

defining
a different importance for each row of the targets, or `1`

-by-`Q`

indicating
a different importance for each sample, or be the scalar 1 (i.e. 1-by-1)
indicating the same importance for all target values.

Similarly, if `outputs`

and `targets`

are
cell arrays of matrices, the `perfWeights`

can be
a cell array of the same size, a row cell array (indicating the relative
importance of each time step), a column cell array (indicating the
relative importance of each neural network output), or a cell array
of a single matrix or just the matrix (both cases indicating that
all matrices have the same importance values).

For any problem, a `perfWeights`

value of `{1}`

(the
default) or the scalar 1 indicates all performances have equal importance.

**Data Types: **`double`

| `cell`

Specify optional comma-separated pairs of `Name,Value`

arguments.
`Name`

is the argument
name and `Value`

is the corresponding
value. `Name`

must appear
inside single quotes (`' '`

).
You can specify several name and value pair
arguments in any order as `Name1,Value1,...,NameN,ValueN`

.

**Example: **`'normalization','standard'`

specifies
the inputs and targets to be normalized to the range (-1,+1).
Proportion of performance attributed to weight/bias values,
specified as a double between 0 (the default) and 1. A larger value
penalizes the network for large weights, and the more likely the network
function will avoid overfitting.

**Example: **`'regularization',0`

**Data Types: **`single`

| `double`

Normalization mode for outputs, targets, and errors, specified
as `'none'`

, `'standard'`

, or `'percent'`

. `'none'`

performs
no normalization. `'standard'`

results in outputs
and targets being normalized to (-1, +1), and therefore errors in
the range (-2, +2).`'percent'`

normalizes outputs
and targets to (-0.5, 0.5) and errors to (-1, 1).

**Example: **`'normalization','standard'`

**Data Types: **`char`