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Machine Learning in Simulink

Extend machine learning workflows using Simulink

Implement machine learning functionality in Simulink® models by using blocks from the Statistics and Machine Learning block library, included in Statistics and Machine Learning Toolbox™. This toolbox provides blocks to perform the following workflows:

  • Import a trained classification or regression model object into Simulink using a classification predict or regression predict block.

  • Train a machine learning model in the Classification Learner or Regression Learner app, and export the model to Simulink.

  • Use incremental learning blocks in Simulink to continuously update and monitor drift in machine learning models in real time.

  • Find nearest neighbors in the data to query points and perform cluster analysis in Simulink using the KNN Search block.

  • Coexecute trained Python® machine learning models in Simulink using Python coexecution blocks.

Blocks

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ClassificationECOC PredictClassify observations using error-correcting output codes (ECOC) classification model (Since R2023a)
ClassificationEnsemble PredictClassify observations using ensemble of decision trees (Since R2021a)
ClassificationKNN PredictClassify observations using nearest neighbor classification model (Since R2022b)
ClassificationLinear PredictClassify observations using linear classification model (Since R2023a)
ClassificationNaiveBayes PredictClassify observations using naive Bayes model (Since R2023b)
ClassificationNeuralNetwork PredictClassify observations using neural network classification model (Since R2021b)
ClassificationSVM PredictClassify observations using support vector machine (SVM) classifier for one-class and binary classification (Since R2020b)
ClassificationTree PredictClassify observations using decision tree classifier (Since R2021a)
ClassificationDiscriminant PredictClassify observations using discriminant analysis model (Since R2024a)
ClassificationKernel PredictClassify observations using Gaussian kernel classifier for binary classification (Since R2024b)
RegressionEnsemble PredictPredict responses using ensemble of decision trees for regression (Since R2021a)
RegressionGP PredictPredict responses using Gaussian process (GP) regression model (Since R2022a)
RegressionLinear PredictPredict responses using linear regression model (Since R2023a)
RegressionNeuralNetwork PredictPredict responses using neural network regression model (Since R2021b)
RegressionSVM PredictPredict responses using support vector machine (SVM) regression model (Since R2020b)
RegressionTree PredictPredict responses using regression tree model (Since R2021a)
RegressionKernel Predict Predict responses using Gaussian kernel regression model (Since R2024b)
IncrementalClassificationLinear PredictClassify observations using incremental linear classification model (Since R2023b)
IncrementalClassificationLinear FitFit incremental linear binary classification model (Since R2023b)
IncrementalRegressionLinear PredictPredict responses using incremental linear regression model (Since R2023b)
IncrementalRegressionLinear FitFit incremental linear regression model (Since R2023b)
IncrementalClassificationECOC FitFit incremental ECOC classification model (Since R2024a)
IncrementalClassificationECOC PredictClassify observations using incremental ECOC classification model (Since R2024a)
IncrementalClassificationKernel FitFit incremental kernel classification model (Since R2024b)
IncrementalClassificationKernel PredictClassify observations using incremental kernel classification model (Since R2024b)
IncrementalRegressionKernel FitFit incremental kernel regression model (Since R2024b)
IncrementalRegressionKernel PredictPredict responses using incremental kernel regression model (Since R2024b)
IncrementalClassificationNaiveBayes FitFit incremental naive Bayes classification model (Since R2025a)
IncrementalClassificationNaiveBayes PredictClassify observations using incremental naive Bayes classification model (Since R2025a)
Detect DriftUpdate drift detector states and drift status with new data (Since R2024b)
Per Observation LossPer observation regression or classification error of incremental model (Since R2025a)
Update MetricsUpdate performance metrics in incremental learning model given new data (Since R2023b)
KNN SearchFind k-nearest neighbors using searcher object (Since R2023b)
Scikit-learn Model PredictPredict responses using pretrained Python scikit-learn model (Since R2024a)
Custom Python Model PredictPredict responses using pretrained custom Python model (Since R2024a)

Topics

Classification

Regression

Incremental Learning

Incremental Learning Templates

Cluster Analysis and Anomaly Detection

Python Coexecution

Export Learner App Models to Simulink

Code Generation

Related Information

Featured Examples