MathWorks Machine Translation
The automated translation of this page is provided by a general purpose third party translator tool.
MathWorks does not warrant, and disclaims all liability for, the accuracy, suitability, or fitness for purpose of the translation.
Standard deviation of input or sequence of inputs
DSP System Toolbox / Statistics
The Standard Deviation block computes the standard deviation of each row or column of the input, or along vectors of a specified dimension of the input. It can also compute the standard deviation of the entire input. You can specify the dimension using the Find the standard deviation value over parameter. The Standard Deviation block can also track the standard deviation in a sequence of inputs over a period of time. To track the standard deviation in a sequence of inputs, select the Running standard deviation parameter.
The Running mode in the Standard Deviation block will be removed in a future release. To compute the running standard deviation in Simulink^{®}, use the Moving Standard Deviation block instead.
In
— Data inputThe block accepts realvalued or complexvalued multichannel and multidimensional inputs.
This port is unnamed until you select the Running standard
deviation parameter and set the Reset
port parameter to any option other than
None
.
Data Types: single
 double
Complex Number Support: Yes
Rst
— Reset portSpecify the reset event that causes the block to reset the running standard deviation. The sample time of the Rst input must be a positive integer multiple of the input sample time.
To enable this port, select the Running standard
deviation parameter and set the Reset
port parameter to any option other than
None
.
Data Types: single
 double
 int8
 int16
 int32
 uint8
 uint16
 uint32
 Boolean
Port_1
— Standard deviation along the specified dimensionThe data type of the output matches the data type of the input.
When you do not select the Running standard
deviation parameter, the block computes the standard
deviation in each row or column of the input, or along vectors of a
specified dimension of the input. It can also compute the standard
deviation of the entire input at each individual sample time. Each
element in the output array y
is the standard
deviation of the corresponding column, row, or entire input. The output
array y
depends on the setting of the Find
the standard deviation value over parameter. Consider a
threedimensional input signal of size
MbyNbyP.
When you set Find the standard deviation value over
to:
Entire input
— The output at
each sample time is a scalar that contains the standard
deviation of the
MbyNbyP
input matrix.
Each row
— The output at
each sample time consists of an
Mby1byP array,
where each element contains the standard deviation of each
vector over the second dimension of the input. For an
MbyN matrix input,
the output at each sample time is an Mby1
column vector.
Each column
— The output at
each sample time consists of a
1byNbyP array,
where each element contains the standard deviation of each
vector over the first dimension of the input. For an
MbyN matrix input,
the output at each sample time is a 1byN
row vector.
In this mode, the block treats lengthM unoriented vector inputs as Mby1 column vectors.
Specified dimension
— The
output at each sample time depends on the value of the
Dimension parameter. If you set the
Dimension to 1
, the
output is the same as when you select Each
column
. If you set the
Dimension to 2
, the
output is the same as when you select Each
row
. If you set the
Dimension to 3
, the
output at each sample time is an
MbyN matrix
containing the standard deviation of each vector over the third
dimension of the input.
When you select Running standard deviation, the block tracks the standard deviation of each channel in a time sequence of inputs. In this mode, you must also specify a value for the Input processing parameter.
Elements as channels (sample based)
— The block treats each element of the input as a
separate channel. For a threedimensional input signal of size
MbyNbyP,
the block outputs an
MbyNbyP
array. Each element
y_{ijk} of the output
contains the standard deviation of the element
u_{ijk} for all
inputs since the last reset.
When a reset event occurs, the running standard deviation y_{ijk} in the current frame is reset to the element u_{ijk}.
Columns as channels (frame based)
— The block treats each column of the input as a separate
channel. This option does not support input signals with more
than two dimensions. For a twodimensional input signal of size
MbyN, the block
outputs an MbyN matrix.
Each element y_{ij} of
the output contains the standard deviation of the elements in
the jth column of all inputs since the last
reset, up to and including the element
u_{ij} of the
current input.
When a reset event occurs, the running standard deviation for each channel becomes the standard deviation of all the samples in the current input frame, up to and including the current input sample.
Data Types: single
 double
Running standard deviation
— Option to select running standard deviationWhen you select the Running standard deviation parameter, the block tracks the standard deviation value of each channel in a time sequence of inputs.
Find the standard deviation value over
— Dimension over which the block computes the standard deviationEach column
(default)  Entire input
 Each row
 Specified dimension
Each column
— The block
outputs the standard deviation over each column.
Each row
— The block
outputs the standard deviation over each row.
Entire input
— The block
outputs the standard deviation over the entire input.
Specified dimension
—
The block outputs the standard deviation over the dimension,
specified in the Dimension
parameter.
To enable this parameter, clear the Running standard deviation parameter.
Dimension
— Custom dimension1
(default)  scalarSpecify the dimension (onebased value) of the input signal over which the standard deviation is computed. The value of this parameter must be greater than 0 and less than the number of dimensions in the input signal.
To enable this parameter, set Find the standard
deviation value over to Specified
dimension
.
Input processing
— Method to process the input in running modeColumns as channels (frame
based)
(default)  Elements as channels (sample
based)
Columns as channels (frame based)
— The block treats each column of the input as a separate
channel. This option does not support input signals with more
than two dimensions. For a twodimensional input signal of size
MbyN, the block
outputs an MbyN matrix.
Each element y_{ij} of
the output contains the standard deviation of the elements in
the jth column of all inputs since the last
reset, up to and including the element
u_{ij} of the
current input.
When a reset event occurs, the running standard deviation for each channel becomes the standard deviation of all the samples in the current input frame, up to and including the current input sample.
Elements as channels (sample based)
— The block treats each element of the input as a
separate channel. For a threedimensional input signal of size
MbyNbyP,
the block outputs an
MbyNbyP
array. Each element
y_{ijk} of the output
contains the standard deviation of the element
u_{ijk} for all
inputs since the last reset.
When a reset event occurs, the running standard deviation y_{ijk} in the current frame is reset to the element u_{ijk}.
VariableSize Inputs
When your inputs are of variable size, and you select the Running standard deviation parameter, then:
If you set the Input
processing parameter to
Elements as channels (sample
based)
, the state is reset.
If you set the Input
processing parameter to
Columns as channels (frame
based)
, then:
When the input size difference is in the number of channels (number of columns), the state is reset.
When the input size difference is in the length of channels (number of rows), no reset occurs and the running operation is carried out as usual.
To enable this parameter, select the Running standard deviation parameter.
Reset port
— Reset eventNone
(default)  Rising edge
 Falling edge
 Either edge
 Nonzero sample
The block resets the running standard deviation whenever a reset event is detected at the optional Rst port. The reset sample time must be a positive integer multiple of the input sample time.
When a reset event occurs while the Input
processing parameter is set to Elements as
channels (sample based)
, the running standard
deviation for each channel is initialized to the value in the
corresponding channel of the current input. Similarly, when the
Input processing parameter is set to
Columns as channels (frame based)
, the
running standard deviation for each channel becomes the standard
deviation of all the samples in the current input frame, up to and
including the current input sample.
Use this parameter to specify the reset event.
None
— Disables the
Rst port.
Rising edge
— Triggers a
reset operation when the Rst input does one
of the following:
Rises from a negative value to either a positive value or zero.
Rises from zero to a positive value, where the rise is not a continuation of a rise from a negative value to zero.
Falling edge
— Triggers a
reset operation when the Rst input does one
of the following:
Falls from a positive value to a negative value or zero.
Falls from zero to a negative value, where the fall is not a continuation of a fall from a positive value to zero.
Either edge
— Triggers a
reset operation when the Rst input is a
Rising edge
or
Falling edge
.
Nonzero sample
— Triggers a
reset operation at each sample time, when the
Rst input is not zero.
When running simulations in the Simulink multitasking mode, reset signals have a onesample latency. Therefore, when the block detects a reset event, there is a onesample delay at the reset port rate before the block applies the reset. For more information on latency and the Simulink tasking modes, see Excess Algorithmic Delay (Tasking Latency) and TimeBased Scheduling and Code Generation (Simulink Coder).
To enable this parameter, select the Running standard deviation parameter.
Data Types 

Direct Feedthrough 

Multidimensional Signals 

VariableSize Signals 

ZeroCrossing Detection 

The standard deviation of a discretetime signal is the square root of the variance of the signal.
Standard deviation gives a measure of deviation of the signal from its mean value.
For purely real or imaginary input, u, of size MbyN, the standard deviation is given by the following equation:
$$y=\sigma =\sqrt{\frac{{\displaystyle \sum _{i=1}^{M}{\displaystyle \sum _{j=1}^{N}{\left{u}_{ij}\right}^{2}\frac{{\left{\displaystyle \sum _{i=1}^{M}{\displaystyle \sum _{j=1}^{N}{u}_{ij}}}\right}^{2}}{M*N}}}}{M*N1}}$$
u_{ij} is the input data element at indices i, j.
M is the length of the jth column.
N is the number of columns.
For complex inputs, the standard deviation is given by the following equation:
$$\sigma =\sqrt{{\sigma}_{\mathrm{Re}}{}^{2}+{\sigma}_{\mathrm{Im}}{}^{2}}$$
σ_{Re}^{2} is the variance of the real part of the complex input.
σ_{Im}^{2} is the variance of the imaginary part of the complex input.
When you clear the Running standard deviation parameter in
the block and specify a dimension, the block produces results identical to the
MATLAB^{®}
std
function, when it is called as y =
std(u,0,D)
.
u
is the data input.
D
is the dimension.
y
is the standard deviation along the specified
dimension.
The standard deviation along the entire input is identical to calling the
std
function as y = std(u(:))
.
For a complex input signal, the standard deviation is the square root of the sum of the variances of the real and imaginary parts.
$$\sigma =\sqrt{{\sigma}_{\mathrm{Re}}{}^{2}+{\sigma}_{\mathrm{Im}}{}^{2}}$$
아래 MATLAB 명령에 해당하는 링크를 클릭하셨습니다.
이 명령을 MATLAB 명령 창에 입력해 실행하십시오. 웹 브라우저에서는 MATLAB 명령을 지원하지 않습니다.
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
Select web siteYou can also select a web site from the following list:
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.