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Applications

R2026a

Appliquer des méthodes statistiques et de Machine Learning à des workflows propres à une industrie

Statistics and Machine Learning Toolbox™ propose des outils pour décrire, analyser et modéliser les données. Ces outils sont décrits dans Démarrer avec Statistics and Machine Learning Toolbox et Machine Learning in MATLAB. Vous pouvez les associer à d’autres toolboxes MATLAB^® pour exécuter des workflows propres à une industrie. Leurs champs d’application sont notamment les suivants :

Aérospatiale : étudiez les signaux radar et autres, détectez les anomalies et créez des modèles prédictifs.
Industries biotechnologiques et pharmaceutiques : analysez les données cliniques et réalisez des modélisations et des simulations pour découvrir et développer de nouveaux médicaments.
Communications et traitement du signal : classifiez les signaux audio et autres, et modélisez des équipements de télécommunications et des circuits intégrés.
Production d’énergie : prévoyez la demande d’énergie, surveillez les équipements de production et optimisez le traitement des produits chimiques pétroliers et gaziers.
Automatisation et machines industrielles : appliquez des statistiques multivariées et la modélisation prédictive aux données des processus industriels, surveillez les procédés de fabrication et la qualité des produits, et améliorez l’utilisation et les rendements.
Équipements médicaux : construisez des algorithmes de Machine Learning interprétables pour les données de séries temporelles et images biomédicales afin de développer des applications tout en respectant les normes réglementaires.
Finance quantitative et gestion du risque : entraînez, comparez et optimisez des modèles pour le trading algorithmique, l’allocation d’actifs, le risque de crédit et la détection des fraudes.

Aérospatiale

Radar Target Classification Using Machine Learning and Deep Learning (Radar Toolbox)

Classify radar returns using machine and deep learning approaches.

Industries biotechnologiques et pharmaceutiques

Séquençage à haut débit

Identifying Differentially Expressed Genes from RNA-Seq Data (Bioinformatics Toolbox)
Use a negative binomial model to test RNA-Seq data for differentially expressed genes.
Exploring Protein-DNA Binding Sites from Paired-End ChIP-Seq Data (Bioinformatics Toolbox)
This example shows how to perform a genome-wide analysis of a transcription factor in the Arabidopsis Thaliana (Thale Cress) model organism.

Découverte de nouveaux médicaments et pharmacologie quantitative des systèmes

Perform PK/PD Modeling and Simulation to Guide Dosing Strategy for Antibiotics (SimBiology)
This example shows how to perform a Monte Carlo simulation of a pharmacokinetic/pharmacodynamic (PK/PD) model for an antibacterial agent.
Simulate the Glucose-Insulin Response (SimBiology)
Simulate and analyze a physiologically based glucose-insulin model for normal and diabetic patients.

Communications et traitement du signal

Data Analysis on S-Parameters of RF Data Files (RF Toolbox)

Perform statistical analysis on S-parameter data files using magnitude, mean, and standard deviation.

Wavelet Time Scattering with GPU Acceleration — Spoken Digit Recognition (Wavelet Toolbox)

Extract features on your GPU for signal classification.

Feature Selection for Audio Classification (Audio Toolbox)

Perform audio feature selection to select a feature set for either speaker recognition or word recognition tasks.

Speaker Identification Using Pitch and MFCC (Audio Toolbox)

Use machine learning to identify people based on features extracted from recorded speech.

Speaker Diarization Using x-vectors (Audio Toolbox)

Speaker diarization is the process of partitioning an audio signal into segments according to speaker identity.

Accelerate Audio Machine Learning Workflows Using a GPU (Audio Toolbox)

This example shows how to use GPU computing to accelerate machine learning workflows for audio, speech, and acoustic applications. (depuis R2024a)

Generate Synthetic Signals Using Conditional GAN (Signal Processing Toolbox)

Use a conditional generative adversarial network to produce synthetic signals.

Human Activity Recognition Using Signal Feature Extraction and Machine Learning (Signal Processing Toolbox)

Extract features from smartphone sensor signals and use them to classify human activity.

Production d'énergie

Analyse prédictive pour la gestion des actifs

Wind Turbine High-Speed Bearing Prognosis (Predictive Maintenance Toolbox)
Build an exponential degradation model to predict the Remaining Useful Life (RUL) of a wind turbine bearing in real time. The exponential degradation model predicts the RUL based on its parameter priors and the latest measurements.

Trading en énergie et gestion des risques (ETRM)

Model and Simulate Electricity Spot Prices Using the Skew-Normal Distribution (Econometrics Toolbox)
This example shows how to simulate the future behavior of electricity spot prices from a time series model fitted to historical data.
Hedging Strategies Using Spread Options (Financial Instruments Toolbox)
This example shows different hedging strategies to minimize exposure in the Energy market using Crack Spread Options.
Pricing Swing Options Using the Longstaff-Schwartz Method (Financial Instruments Toolbox)
This example shows how to price a swing option using a Monte Carlo simulation and the Longstaff-Schwartz method.

Automatisation et machines industrielles

Fault Detection Using Data Based Models (Predictive Maintenance Toolbox)

Use a data-based modeling approach for fault detection.

Detect Anomalies in Industrial Machinery Using Three-Axis Vibration Data (Predictive Maintenance Toolbox)

Detect anomalies in industrial machine vibration data using machine-learning and deep-learning models trained with data representing only nominal behavior.

Build Condition Model for Industrial Machinery and Manufacturing Processes

Train a binary classification model using Classification Learner App to detect anomalies in sensor data collected from an industrial manufacturing machine.

Rolling Element Bearing Fault Diagnosis (Predictive Maintenance Toolbox)

Perform fault diagnosis of a rolling element bearing based on acceleration signals.

Fault Diagnosis of Centrifugal Pumps Using Residual Analysis (Predictive Maintenance Toolbox)

Use a model parity-equations-based approach for detection and diagnosis of faults in a pumping system.

Air Compressor Fault Detection Using Wavelet Scattering (Wavelet Toolbox)

Classify faults in acoustic recordings of air compressors using a wavelet scattering network and a support vector machine.

Predict Battery State of Charge Using Machine Learning

Train a Gaussian process regression model to predict the state of charge of a battery in automotive engineering.

Deploy Neural Network Regression Model to FPGA/ASIC Platform

Predict in Simulink^® using a neural network regression model, and deploy the Simulink model to an FPGA/ASIC platform by using HDL code generation.

Monitor Equipment State of Health Using Drift-Aware Learning

This example shows how to automate the process of monitoring the state of health for a cooling system using an incremental drift-aware learning model and Streaming Data Framework for MATLAB® Production Server™.

Monitor Equipment State of Health Using Drift-Aware Learning on the Cloud

This example describes the set up necessary to run the deployed version of the Monitor Equipment State of Health Using Drift-Aware Learning example on the cloud.

Équipements médicaux

Wavelet Time Scattering for ECG Signal Classification (Wavelet Toolbox)

Classify human electrocardiogram signals using wavelet time scattering and a support vector machine classifier.

Wavelet Time Scattering Classification of Phonocardiogram Data (Wavelet Toolbox)

Classify human phonocardiogram recordings using wavelet time scattering and a support vector machine classifier.

Human Activity Recognition Simulink Model for Smartphone Deployment

Generate code from a classification Simulink model prepared for deployment to a smartphone.

Human Activity Recognition Simulink Model for Fixed-Point Deployment

Generate code from a classification Simulink model prepared for fixed-point deployment.

Finance quantitative et gestion du risque

Trading algorithmique

Machine Learning for Statistical Arbitrage: Introduction (Financial Toolbox)
Get an overview of the workflow for statistical arbitrage and then follow a series of examples to see how capabilities in MATLAB apply.
Machine Learning for Statistical Arbitrage II: Feature Engineering and Model Development (Financial Toolbox)
Create a continuous-time Markov model of limit order book (LOB) dynamics, and develop a strategy for algorithmic trading based on patterns observed in the data.

Risque de crédit

Forecasting Corporate Default Rates (Financial Toolbox)
This example shows how to build a forecasting model for corporate default rates.
Credit Scoring Using Logistic Regression and Decision Trees (Risk Management Toolbox)
This example shows how to create and compare two credit scoring models, which includes:

Optimisation des portefeuilles et allocation d'actifs

Portfolio Optimization Using Factor Models (Financial Toolbox)
This example shows two approaches for using a factor model to optimize asset allocation under a mean-variance framework.

Modélisation économétrique

Time Series Regression I: Linear Models (Econometrics Toolbox)
This example introduces basic assumptions behind multiple linear regression models.
Time Series Regression III: Influential Observations (Econometrics Toolbox)
This example shows how to detect influential observations in time series data and accommodate their effect on multiple linear regression models.

Sélection d՚exemples

Predict Battery State of Charge Using Machine Learning

Train a Gaussian process regression model to predict the state of charge of a battery in automotive engineering.

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Deploy Neural Network Regression Model to FPGA/ASIC Platform

Predict in Simulink using a neural network regression model, and deploy the Simulink model to an FPGA/ASIC platform by using HDL code generation.

Ouvrir le live script

Monitor Equipment State of Health Using Drift-Aware Learning on the Cloud

Describes the set up necessary to run the deployed version of the Monitor Equipment State of Health Using Drift-Aware Learning example on the cloud. The topic shows how to automate the process of monitoring the state of health for a cooling system using an incremental drift-aware learning model using the infrastructure in the next figure. This example requires Statistics and Machine Learning Toolbox™, MATLAB® Compiler SDK™, MATLAB Production Server™, and MATLAB Web App Server™.

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Human Activity Recognition Simulink Model for Smartphone Deployment

Generate code from a classification Simulink model prepared for deployment to a smartphone.

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Ressources pédagogiques

Machine Learning for Biosciences (Machine Learning pour les biosciences)

Découvrir les fondamentaux du Machine Learning avec des exemples issus de la biologie.