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Find mean value of input or sequence of inputs
DSP System Toolbox / Statistics
The Mean block computes the mean of each row or column of the input, or along vectors of a specified dimension of the input. It can also compute the mean of the entire input. You can specify the dimension using the Find the mean value over parameter. The Mean block can also track the mean value in a sequence of inputs over a period of time. To track the mean value in a sequence of inputs, select the Running mean parameter.
Note: The Running mode in the Mean block will be removed in a future release. To compute the running mean in Simulink^{®}, use the Moving Average block instead. |
I
— Data inputThe block accepts real-valued or complex-valued multichannel and multidimensional inputs. The input data type must be double precision, single precision, integer, or fixed point with power-of-two slope and zero bias.
This port is unnamed until you select the Running
mean parameter and set the Reset port parameter
to any option other than None
.
Data Types: single
| double
| int8
| int16
| int32
| uint8
| uint16
| uint32
| fixed_point
Complex Number Support: Yes
Rst
— Reset portSpecify the reset event that causes the block to reset the running mean. 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 mean 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
— Mean value along the specified dimensionThe data type of the output matches the data type of the input.
When you do not select the Running mean parameter,
the block computes the mean value in each row or column of the input,
or along vectors of a specified dimension of the input. It can also
compute the mean of the entire input at each individual sample time.
Each element in the output array y
is the mean
value of the corresponding column, row, or entire input. The output
array y
depends on the setting of the Find
the mean value over parameter. Consider a three-dimensional
input signal of size M-by-N-by-P.
When you set Find the mean value over to:
Entire input
— The
output at each sample time is a scalar that contains the mean value
of the M-by-N-by-P input
matrix.
Each row
— The output
at each sample time consists of an M-by-1-by-P array,
where each element contains the mean value of each vector over the
second dimension of the input. For an M-by-N matrix
input, the output at each sample time is an M-by-1
column vector.
Each column
— The
output at each sample time consists of a 1-by-N-by-P array,
where each element contains the mean value of each vector over the
first dimension of the input. For an M-by-N matrix
input, the output at each sample time is a 1-by-N row
vector.
In this mode, the block treats length-M unoriented vector inputs as M-by-1 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 M-by-N matrix
containing the mean value of each vector over the third dimension
of the input.
When you select Running mean, the block tracks the mean value 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 three-dimensional input signal of size M-by-N-by-P,
the block outputs an M-by-N-by-P array.
Each element y_{ijk} of the
output contains the mean value of the element u_{ijk} for
all inputs since the last reset.
When a reset event occurs, the running mean 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 an N-dimensional input signal, where N >
2. For a two-dimensional input signal of size M-by-N,
the block outputs an M-by-N matrix.
Each element y_{ij} of the
output contains the mean of the values 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 mean for each channel becomes the mean value of all the samples in the current input frame, up to and including the current input sample.
Data Types: single
| double
| int8
| int16
| int32
| uint8
| uint16
| uint32
| fixed_point
Complex Number Support: Yes
Running mean
— Option to select running meanWhen you select the Running mean parameter, the block tracks the mean value of each channel in a time sequence of inputs.
Find the mean value over
— Dimension over which the block computes the mean valueEach column
(default) | Entire input
| Each row
| Specified dimension
Each column
— The
block outputs the mean value over each column.
Each row
— The block
outputs the mean value over each row.
Entire input
— The
block outputs the mean value over the entire input.
Specified dimension
—
The block outputs the mean value over the dimension, specified in
the Dimension parameter.
To enable this parameter, clear the Running mean parameter.
Dimension
— Custom dimension1
(default) | scalarSpecify the dimension (one-based value) of the input signal over which the mean 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 mean 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 an N-dimensional input signal, where N >
2. For a two-dimensional input signal of size M-by-N,
the block outputs an M-by-N matrix.
Each element y_{ij} of the
output contains the mean of the values 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 mean for each channel becomes the mean value 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 three-dimensional input signal of size M-by-N-by-P,
the block outputs an M-by-N-by-P array.
Each element y_{ijk} of the
output contains the mean value of the element u_{ijk} for
all inputs since the last reset.
When a reset event occurs, the running mean y_{ijk} in the current frame is reset to the element u_{ijk}.
Variable-Size Inputs
When your inputs are of variable size, and you select the Running mean 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), there is no reset and the running operation is carried out as usual.
To enable this parameter, select the Running mean parameter.
Reset port
— Reset eventNone
(default) | Rising edge
| Falling edge
| Either edge
| Non-zero sample
The block resets the running mean 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 mean 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 mean for each channel
becomes the mean value 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
.
Non-zero sample
—
Triggers a reset operation at each sample time, when the Rst input
is not zero.
Note: When running simulations in the Simulink multitasking mode, reset signals have a one-sample latency. Therefore, when the block detects a reset event, there is a one-sample 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 Time-Based Scheduling and Code Generation (Simulink Coder). |
To enable this parameter, select the Running mean parameter.
Note: To use these parameters, the data input must be fixed-point. For all other inputs, the parameters on the Data Types tab are ignored. |
Rounding mode
— Method of rounding operationFloor
(default) | Ceiling
| Convergent
| Nearest
| Round
| Simplest
| Zero
Select the rounding mode for fixed-point operations.
Overflow mode
— Method of overflow actionWrap
(default) | Saturate
Select the overflow mode for fixed-point operations.
Accumulator
— Accumulator data typeInherit: Same as input
(default) | fixdt([],16,0)
Accumulator specifies the data type of the output of an accumulation operation in the Mean block. See Fixed Point for illustrations depicting the use of the accumulator data type in this block. You can set this parameter to:
Inherit: Same as input
—
The block specifies the accumulator data type to be the same as the
input data type.
fixdt([],16,0)
—
The block specifies an autosigned, binary-point, scaled, fixed-point
data type with a word length of 16 bits and a fraction length of 0.
Alternatively, you can set the Accumulator data type by using the Data Type Assistant. To use the assistant, click the Show data type assistant button .
For more information on the data type assistant, see Specify Data Types Using Data Type Assistant (Simulink).
Output
— Output data typeInherit: Same as accumulator
(default) | fixdt([],16,0)
Output specifies the data type of the output of the Mean block. See Fixed Point for illustrations depicting the use of the output data type in this block. You can set it to:
Inherit: Same as accumulator
—
The block specifies the output data type to be the same as the accumulator
data type.
fixdt([],16,0)
—
The block specifies an autosigned, binary-point, scaled, fixed-point
data type with a word length of 16 bits and a fraction length of 0.
Alternatively, you can set the Output data type by using the Data Type Assistant. To use the assistant, click the Show data type assistant button .
For more information on the data type assistant, see Specify Data Types Using Data Type Assistant (Simulink).
Output Minimum
— Minimum value the block can output[]
(default) | scalarSpecify the minimum value that the block can output. The default
value is []
(unspecified). Simulink uses this
value to perform:
Simulation range checking. See Signal Ranges (Simulink).
Automatic scaling of fixed-point data types.
Output Maximum
— Maximum value the block can output[]
(default) | scalarSpecify the maximum value that the block can output. The default
value is []
(unspecified). Simulink uses this
value to perform:
Simulation range checking. See Signal Ranges (Simulink).
Automatic scaling of fixed-point data types.
Lock data type settings against changes by the fixed-point tools
— Prevent fixed-point tools from overriding data typesSelect this parameter to prevent the fixed-point tools from overriding the data types you specify on the block.
When you clear the Running mean parameter
and specify a dimension, the block produces results identical to the MATLAB^{®} mean
function,
when it is called as y = mean(u,D)
.
u
is the data input.
D
is the dimension.
y
is the mean value.
The mean value along the entire input is identical to calling
the mean
function as y = mean(u(:))
.
The mean of a complex input is computed independently for the real and imaginary components.
When you select the Running mean parameter,
and set the Input processing parameter to Columns
as channels (frame based)
, the block treats each column
of the input as a separate channel. In this example, the block processes
a two-channel signal with a frame size of three under these settings.
The block outputs the mean value over each channel since the last reset. At t = 2, the reset event occurs. The window of data in the second column now contains only 6.
When you select the Running mean parameter,
and set the Input processing parameter to Elements
as channels (sample based)
, the block treats each element
of the input as a separate channel. In this example, the block processes
a two-channel signal with a frame size of three under these settings.
Each y_{ij} element of the output contains the mean value observed in element u_{ij} for all inputs since the last reset. The reset event occurs at t = 2. When a reset event occurs, the running mean, y_{ij}, in the current frame is reset to element u_{ij}.
The following diagram shows the data types used within the Mean block for fixed-point signals.
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