RegressionNeuralNetwork Predict - Predict responses using neural network regression model - Simulink (original) (raw)

Predict responses using neural network regression model

Since R2021b

Libraries:
Statistics and Machine Learning Toolbox / Regression

Description

The RegressionNeuralNetwork Predict block predicts responses using a neural network regression object (RegressionNeuralNetwork or CompactRegressionNeuralNetwork).

Import a trained regression object into the block by specifying the name of a workspace variable that contains the object. The input port x receives an observation (predictor data), and the output port yfit returns a predicted response for the observation.

Examples

Ports

Input

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Data Types: single | double | half | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | Boolean | fixed point

Output

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Predicted response, returned as a scalar.

Data Types: single | double | half | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | Boolean | fixed point

Parameters

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Main

Data Types

Fixed-Point Operational Parameters

Data Type

Specify the data type for the output layer. The type can be inherited, specified directly, or expressed as a data type object such asSimulink.NumericType.

When you select Inherit: Inherit via internal rule, the block uses an internal rule to determine the output data type. The internal rule chooses a data type that optimizes numerical accuracy, performance, and generated code size, while taking into account the properties of the embedded target hardware. The software cannot always optimize efficiency and numerical accuracy at the same time.

For more information about data types, see Control Data Types of Signals (Simulink).

Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).

Programmatic Use

Specify the lower value of the output layer's internal variable range checked by Simulink.

Simulink uses the minimum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

Note

The Output layer data type Minimum parameter does not saturate or clip the output layer value signal.

Programmatic Use

Specify the upper value of the output layer's internal variable range checked by Simulink.

Simulink uses the maximum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

Note

The Output layer data type Maximum parameter does not saturate or clip the output layer value signal.

Programmatic Use

Specify the data type for the first layer. The type can be inherited, specified directly, or expressed as a data type object such asSimulink.NumericType.

When you select Inherit: Inherit via internal rule, the block uses an internal rule to determine the data type. The internal rule chooses a data type that optimizes numerical accuracy, performance, and generated code size, while taking into account the properties of the embedded target hardware. The software cannot always optimize efficiency and numerical accuracy at the same time.

For more information about data types, see Control Data Types of Signals (Simulink).

Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).

Tips

A trained neural network can have more than one fully connected layer, excluding the output layer.

• You can specify the data type for each individual layer for the first 10 layers. Specify the data type Layer n data type for each layer. The data type of the first layer is Layer 1 data type, the data type of the second layer is Layer 2 data type, and so on.
• You can specify the data type for layers 11 to k, where_k_ is the total number of layers, by using the data type Additional layer(s) data type. The Block Parameter for Additional layer(s) data type is Layer11DataTypeStr.
• The data types Layer n data type andAdditional layer(s) data type can be inherited, specified directly, or expressed as a data type object such asSimulink.NumericType. These data types support the same values as Layer 1 data type.

Programmatic Use

Specify the lower value of the first layer's internal variable range checked by Simulink.

Simulink uses the minimum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

Note

The Layer 1 data type Minimum parameter does not saturate or clip the first layer value signal.

Tips

A trained neural network can have more than one fully connected layer, excluding the output layer.

• You can specify the lower value of each individual layer's internal variable range checked by Simulink for the first 10 layers. Specify the lower valueLayer n minimum for each layer. The minimum value of the first layer is Layer 1 minimum, the minimum value of the second layer is Layer 2 minimum, and so on.
• You can specify the lower value for layers 11 to k, where k is the total number of layers, by usingAdditional layer(s) minimum. The Block Parameter for Additional layer(s) minimum is Layer11OutMin.
• Layer n minimum and Additional layer(s) minimum support the same values as Layer 1 minimum.

Programmatic Use

Specify the upper value of the first layer's internal variable range checked by Simulink.

Simulink uses the maximum value to perform:

• Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
• Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
• Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).

Note

The Layer 1 data type Maximum parameter does not saturate or clip the first layer value signal.

Tips

A trained neural network can have more than one fully connected layer, excluding the output layer.

• You can specify the upper value of each individual layer's internal variable range checked by Simulink for the first 10 layers. Specify the upper valueLayer n maximum for each layer. The maximum value of the first layer is Layer 1 maximum, the maximum value of the second layer is Layer 2 maximum, and so on.
• You can specify the upper value for layers 11 to k, where k is the total number of layers, by usingAdditional layer(s) maximum. The Block Parameter for Additional layer(s) maximum is Layer11OutMax.
• Layer n maximum and Additional layer(s) maximum support the same values as Layer 1 maximum.

Programmatic Use

Block Characteristics

More About

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The data types of internal model parameters are synchronized to the data type of the output port, yfit.

Alternative Functionality

You can use a MATLAB Function block with the predict object function of a neural network regression object (RegressionNeuralNetwork or CompactRegressionNeuralNetwork). For an example, see Predict Class Labels Using MATLAB Function Block.

When deciding whether to use the RegressionNeuralNetwork Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB Function block with the predict function, consider the following:

• If you use the Statistics and Machine Learning Toolbox library block, you can use the Fixed-Point Tool (Fixed-Point Designer) to convert a floating-point model to fixed point.
• Support for variable-size arrays must be enabled for a MATLAB Function block with the predict function.
• If you use a MATLAB Function block, you can use MATLAB functions for preprocessing or post-processing before or after predictions in the same MATLAB Function block.

Extended Capabilities

Version History

Introduced in R2021b

See Also

Blocks

• RegressionSVM Predict | RegressionTree Predict | RegressionEnsemble Predict | RegressionGP Predict | ClassificationNeuralNetwork Predict

Objects

• RegressionNeuralNetwork | CompactRegressionNeuralNetwork

Functions

• predict | fitrnet

Topics

• Predict Responses Using RegressionSVM Predict Block
• Predict Responses Using RegressionTree Predict Block
• Predict Responses Using RegressionEnsemble Predict Block
• Predict Responses Using RegressionGP Predict Block
• Predict Class Labels Using MATLAB Function Block
• Deploy Neural Network Regression Model to FPGA/ASIC Platform