Mean

Find mean value of input or sequence of inputs

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Libraries:
DSP System Toolbox / Statistics

Description

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.

Examples

Compute the Mean

Simulink model example to compute the mean using the Mean block.

• Open Script

Compute the Running Mean

Simulink model example to compute the running mean using the Mean block.

• Open Script

Compute Mean Using Sliding Window

Model a sliding window using the Buffer block. The Mean block use this window to compute the mean.

• Open Model

Ports

Input

I — Data input

vector | matrix | N-D array

The 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 | int64 | uint8 | uint16 | uint32 | uint64 | fixed point
Complex Number Support: Yes

Rst — Reset port

scalar

Specify 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.

Dependencies

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

Output

Port_1 — Mean value along the specified dimension

scalar | vector | matrix | N-D array

The 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 | int64 | uint8 | uint16 | uint32 | uint64 | fixed point
Complex Number Support: Yes

Parameters

Main Tab

Running mean — Option to select running mean

off (default) | on

When 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 value

Each 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.

Dependencies

To enable this parameter, clear the Running mean parameter.

Dimension — Custom dimension

1 (default) | scalar

Specify 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.

Dependencies

To enable this parameter, set Find the mean value over to Specified dimension.

Input processing — Method to process the input in running mode

Columns 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.

Dependencies

To enable this parameter, select the Running mean parameter.

Reset port — Reset event

None (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).

Dependencies

To enable this parameter, select the Running mean parameter.

Data Types Tab

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 operation

Floor (default) | Ceiling | Convergent | Nearest | Round | Simplest | Zero

Specify the rounding mode for fixed-point operations as one of the following:

• Floor

• Ceiling

• Convergent

• Nearest

• Round

• Simplest

• Zero

For more details, see rounding mode.

Saturate on integer overflow — Method of overflow action

off (default) | on

When you select this parameter, the block saturates the result of its fixed-point operation. When you clear this parameter, the block wraps the result of its fixed-point operation. For details on saturate and wrap, see overflow mode for fixed-point operations.

Accumulator — Accumulator data type

Inherit: 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 Data Types 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 type

Inherit: Same as accumulator (default) | fixdt([],16,0)

Output specifies the data type of the output of the Mean block. See Fixed-Point Data Types 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) | scalar

Specify the minimum value that the block can output. The default value is [] (unspecified). Simulink uses this value to perform:

• Simulation range checking. See Specify Signal Ranges (Simulink).

• Automatic scaling of fixed-point data types.

Output Maximum — Maximum value the block can output

[] (default) | scalar

Specify the maximum value that the block can output. The default value is [] (unspecified). Simulink uses this value to perform:

• Simulation range checking. See Specify 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 types

off (default) | on

Select this parameter to prevent the fixed-point tools from overriding the data types you specify on the block.

Block Characteristics

Data Types	double | fixed point | integer | single
Direct Feedthrough	no
Multidimensional Signals	no
Variable-Size Signals	yes
Zero-Crossing Detection	no

More About

Fixed-Point Data Types

The following diagram shows the data types used within the Mean block for fixed-point signals.

Algorithms

Mean

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.

Running Mean

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.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

Fixed-Point Conversion
Design and simulate fixed-point systems using Fixed-Point Designer™.

Version History

Introduced before R2006a