Machine learning en Simulink
R2025bAmplíe los flujos de trabajo de machine learning usando Simulink
Aplique funcionalidades de machine learning en modelos de Simulink® usando bloques de la biblioteca de bloques Statistics and Machine Learning, incluida en Statistics and Machine Learning Toolbox™. Esta toolbox proporciona bloques para realizar los siguientes flujos de trabajo:
Importar un objeto de modelo de regresión o clasificación entrenado a Simulink usando un bloque de predicción de regresión o de predicción de clasificación.
Entrenar un modelo de machine learning en la app Classification Learner o Regression Learner y exportar el modelo a Simulink.
Utilizar bloques de aprendizaje incremental en Simulink para actualizar continuamente y monitorizar la desviación en modelos de machine learning en tiempo real.
Encontrar los vecinos más cercanos en los datos a puntos de consulta y realizar análisis de clusters en Simulink usando el bloque KNN Search.
Coejecutar modelos entrenados de machine learning de Python® entrenados en Simulink utilizando bloques de coejecución de Python.
Bloques
 | ClassificationECOC Predict | Classify observations using error-correcting output codes (ECOC) classification model (Desde R2023a) |
 | ClassificationEnsemble Predict | Classify observations using ensemble of decision trees |
 | ClassificationKNN Predict | Classify observations using nearest neighbor classification model (Desde R2022b) |
 | ClassificationLinear Predict | Classify observations using linear classification model (Desde R2023a) |
 | ClassificationNaiveBayes Predict | Classify observations using naive Bayes model (Desde R2023b) |
 | ClassificationNeuralNetwork Predict | Classify observations using neural network classification model |
 | ClassificationSVM Predict | Classify observations using support vector machine (SVM) classifier for one-class and binary classification |
 | ClassificationTree Predict | Classify observations using decision tree classifier |
 | ClassificationDiscriminant Predict | Classify observations using discriminant analysis model (Desde R2024a) |
 | ClassificationKernel Predict | Classify observations using Gaussian kernel classifier for binary classification (Desde R2024b) |
 | RegressionEnsemble Predict | Predict responses using ensemble of decision trees for regression |
 | RegressionGP Predict | Predict responses using Gaussian process (GP) regression model (Desde R2022a) |
 | RegressionLinear Predict | Predecir respuestas usando el modelo de regresión lineal (Desde R2023a) |
 | RegressionNeuralNetwork Predict | Predict responses using neural network regression model |
 | RegressionSVM Predict | Predecir las respuestas mediante un modelo de regresión de máquina de vectores de soporte (SVM) |
 | RegressionTree Predict | Predecir respuestas usando un modelo de árboles de regresión |
 | RegressionKernel Predict | Predict responses using Gaussian kernel regression model (Desde R2024b) |
 | IncrementalClassificationLinear Predict | Classify observations using incremental linear classification model (Desde R2023b) |
 | IncrementalClassificationLinear Fit | Fit incremental linear binary classification model (Desde R2023b) |
 | IncrementalRegressionLinear Predict | Predict responses using incremental linear regression model (Desde R2023b) |
 | IncrementalRegressionLinear Fit | Fit incremental linear regression model (Desde R2023b) |
 | IncrementalClassificationECOC Fit | Fit incremental ECOC classification model (Desde R2024a) |
 | IncrementalClassificationECOC Predict | Classify observations using incremental ECOC classification model (Desde R2024a) |
 | IncrementalClassificationKernel Fit | Fit incremental kernel classification model (Desde R2024b) |
 | IncrementalClassificationKernel Predict | Classify observations using incremental kernel classification model (Desde R2024b) |
 | IncrementalRegressionKernel Fit | Fit incremental kernel regression model (Desde R2024b) |
 | IncrementalRegressionKernel Predict | Predict responses using incremental kernel regression model (Desde R2024b) |
 | IncrementalClassificationNaiveBayes Fit | Fit incremental naive Bayes classification model (Desde R2025a) |
 | IncrementalClassificationNaiveBayes Predict | Classify observations using incremental naive Bayes classification model (Desde R2025a) |
 | Detect Drift | Actualizar estados de un detector de deriva y el estado de deriva con nuevos datos (Desde R2024b) |
 | Per Observation Loss | Per observation regression or classification error of incremental model (Desde R2025a) |
 | Update Metrics | Update performance metrics in incremental learning model given new data (Desde R2023b) |
 | KNN Search | Encontrar los k vecinos más cercanos utilizando un objeto de búsqueda (Desde R2023b) |
 | Scikit-learn Model Predict | Predict responses using pretrained Python scikit-learn model (Desde R2024a) |
 | Custom Python Model Predict | Predict responses using pretrained custom Python model (Desde R2024a) |
Temas
Clasificación
- Predict Class Labels Using ClassificationSVM Predict Block
This example shows how to use the ClassificationSVM Predict block for label prediction in Simulink®. - Predict Class Labels Using ClassificationTree Predict Block
Train a classification decision tree model using the Classification Learner app, and then use the ClassificationTree Predict block for label prediction. - Predict Class Labels Using ClassificationLinear Predict Block
This example shows how to use the ClassificationLinear Predict block for label prediction in Simulink®. (Desde R2023a) - Predict Class Labels Using ClassificationECOC Predict Block
Train an ECOC classification model, and then use the ClassificationECOC Predict block for label prediction. (Desde R2023a) - Predict Class Labels Using ClassificationEnsemble Predict Block
Train a classification ensemble model with optimal hyperparameters, and then use the ClassificationEnsemble Predict block for label prediction. - Predict Class Labels Using ClassificationNaiveBayes Predict Block
Train a naive Bayes classification model, and then use the ClassificationNaiveBayes Predict block for label prediction. (Desde R2024a) - Predict Class Labels Using ClassificationNeuralNetwork Predict Block
Train a neural network classification model, and then use the ClassificationNeuralNetwork Predict block for label prediction. - Predict Class Labels Using ClassificationKNN Predict Block
Train a nearest neighbor classification model, and then use the ClassificationKNN Predict block for label prediction. - Predict Class Labels Using ClassificationDiscriminant Predict Block
Train a discriminant analysis classification model, and then use the ClassificationDiscriminant Predict block for label prediction. (Desde R2023b) - Predict Class Labels Using ClassificationKernel Predict Block
Train a Gaussian kernel classification model, and then use the ClassificationKernel Predict block for label prediction. (Desde R2024b)
Regresión
- Predict Responses Using RegressionSVM Predict Block
Train a support vector machine (SVM) regression model using the Regression Learner app, and then use the RegressionSVM Predict block for response prediction. - Predict Responses Using RegressionTree Predict Block
This example shows how to use the RegressionTree Predict block for response prediction in Simulink®. - Predict Responses Using RegressionLinear Predict Block
This example shows how to use the RegressionLinear Predict block for response prediction in Simulink®. (Desde R2023a) - Predict Responses Using RegressionEnsemble Predict Block
Train a regression ensemble model with optimal hyperparameters, and then use the RegressionEnsemble Predict block for response prediction. - Predict Responses Using RegressionNeuralNetwork Predict Block
Train a neural network regression model, and then use the RegressionNeuralNetwork Predict block for response prediction. - Predict Responses Using RegressionGP Predict Block
Train a Gaussian process (GP) regression model, and then use the RegressionGP Predict block for response prediction. - Predict Responses Using RegressionKernel Predict Block
This example shows how to use the RegressionKernel Predict block for response prediction in Simulink®. (Desde R2024b)
Aprendizaje incremental
- Perform Incremental Learning Using IncrementalClassificationLinear Fit and Predict Blocks
Perform incremental learning with the IncrementalClassificationLinear Fit block and predict labels with the IncrementalClassificationLinear Predict block. (Desde R2023b) - Perform Incremental Learning Using IncrementalRegressionLinear Fit and Predict Blocks
Perform incremental learning with the IncrementalRegressionLinear Fit block and predict responses with the IncrementalRegressionLinear Predict block. (Desde R2023b) - Perform Incremental Learning Using IncrementalClassificationECOC Fit and Predict Blocks
Perform incremental learning with the IncrementalClassificationECOC Fit block and predict labels with the IncrementalClassificationECOC Predict block. (Desde R2024a) - Perform Incremental Learning Using IncrementalClassificationKernel Fit and Predict Blocks
Perform incremental learning with the IncrementalClassificationKernel Fit block and predict labels with the IncrementalClassificationKernel Predict block. (Desde R2024b) - Perform Incremental Learning Using IncrementalRegressionKernel Fit and Predict Blocks
Perform incremental learning with the IncrementalRegressionKernel Fit block and predict responses with the IncrementalRegressionKernel Predict block. (Desde R2024b) - Perform Incremental Learning and Track Performance Metrics Using Update Metrics Block
Perform incremental learning and track performance metrics with the Update Metrics block. (Desde R2023b) - Monitor Drift Using Detect Drift Block
This example shows how to use the Detect Drift block for monitoring drift in a data stream in Simulink®. (Desde R2024b) - In-Place Model Update of Offline Linear Model Using IncrementalClassificationLinear Predict Block
Perform in-place model update without regenerating deployed code. (Desde R2025a)
Plantillas de aprendizaje incremental
- Configure Simulink Template for Conditionally Enabled Incremental Linear Classification
Configure the Simulink Enabled Execution Incremental Learning template to perform incremental linear classification. (Desde R2024a) - Configure Simulink Template for Conditionally Enabled Incremental Linear Regression
Configure the Simulink Enabled Execution Incremental Learning template to perform incremental linear regression. (Desde R2024a) - Configure Simulink Template for Rate-Based Incremental Linear Classification
Configure the Simulink Rate-Based Incremental Learning template to perform incremental linear classification. (Desde R2024a) - Configure Simulink Template for Rate-Based Incremental Linear Regression
Configure the Simulink Rate-Based Incremental Learning template to perform incremental linear regression. (Desde R2024a) - Configure Simulink Template for Drift-Aware Incremental Learning
Configure the Drift-Aware Training for Incremental Learning template to perform drift-aware learning. (Desde R2025a)
Análisis de clusters y detección de anomalías
- Find Nearest Neighbors Using KNN Search Block
Train a nearest neighbor searcher model, and then use the KNN Search block for label prediction. (Desde R2023b)
Coejecución de Python
- Predict Cluster Assignments Using Python Scikit-learn Model Predict Block
This example shows how to use the Scikit-learn Model Predict block for prediction in Simulink®. - Predict Responses Using Custom Python Model in Simulink
This example shows how to use the Custom Python Model Predict block for prediction in Simulink®.
Exportar modelos de apps Learner a Simulink
- Export Classification Model to Make Predictions in Simulink
After training a model in Classification Learner, export the model to Simulink. - Export Regression Model to Make Predictions in Simulink
After training a model in Regression Learner, export the model to Simulink.
Generación de código
- System Objects for Classification and Code Generation
Generate code from a System object™ for making predictions using a trained classification model, and use the System object in a Simulink model. - Predict Class Labels Using MATLAB Function Block
Generate code from a Simulink model that classifies data using an SVM model. - Predict Class Labels Using Stateflow
Generate code from a Stateflow® model that classifies data using a discriminant analysis classifier.
Información relacionada
- Deep learning con Simulink (Deep Learning Toolbox)
Ejemplos destacados
Human Activity Recognition Simulink Model for Fixed-Point Deployment
Generate code from a classification Simulink model prepared for fixed-point deployment.
Human Activity Recognition Simulink Model for Smartphone Deployment
Generate code from a classification Simulink model prepared for deployment to a smartphone.
Identify Punch and Flex Hand Gestures Using Machine Learning Algorithm on Arduino Hardware
Use the Simulink® Support Package for Arduino® Hardware to identify punch and flex hand gestures using a machine learning algorithm. The example is deployed on an Arduino Nano 33 IoT hardware board that uses an onboard LSM6DS3 6DOF IMU sensor to identify the hand gestures. The output from the machine learning algorithm, after identifying whether a hand gesture is a punch or a flex, is transmitted to the serial port where 0 represents a punch and 1 represents a flex.
Compress Machine Learning Model for Memory-Limited Hardware
Reduce model size by feature selection, constrained Bayesian optimization, and parameter quantization.
