End-to-End AI Workflows

Use deep learning in end-to-end tasks including defining requirements, data preparation, deep neural training, compression, network testing and verification, Simulink integration, and deployment

Use Deep Learning Toolbox™ in end-to-end workflows that include defining requirements, data preparation, deep neural training, compression, network testing and verification, Simulink integration, and deployment.

Topics

Battery State of Charge Estimation Using Deep Learning

Define requirements, prepare data, train deep learning networks, verify robustness, integrate networks into Simulink, and deploy models. (Since R2024b)
- STEP 1: Define Requirements for Battery State of Charge Estimation
- STEP 2: Prepare Data for Battery State of Charge Estimation Using Deep Learning
- STEP 3: Train Deep Learning Network for Battery State of Charge Estimation
- STEP 4: Compress Deep Learning Network for Battery State of Charge Estimation
- STEP 5: Test Deep Learning Network for Battery State of Charge Estimation
- STEP 6: Integrate AI Model into Simulink for Battery State of Charge Estimation
- STEP 7: Generate Code for Battery State of Charge Estimation Using Deep Learning
Verify an Airborne Deep Learning System
This example shows how to verify a deep learning system for airborne applications and is based on the work in [5,6,7], which includes the development and verification activities required by DO-178C [1], ARP4754A [2], and prospective EASA and FAA guidelines [3,4]. (Since R2023b)

Featured Examples

Field-Oriented Control of PMSM Using Position Estimated by Neural Network

Implement field-oriented control (FOC) of a permanent magnet synchronous motor (PMSM) using rotor position estimated by an auto-regressive neural network (ARNN) trained with Deep Learning Toolbox™.

(Motor Control Blockset)