Durchgängige KI-Workflows
Verwenden Sie die Deep Learning Toolbox™ in durchgängigen Workflows einschließlich dem Festlegen von Anforderungen, der Datenvorbereitung, neuronalem Deep Training, Komprimierung, Netz-Tests und -Verifikation, Simulink-Integration und Bereitstellung.
Themen
- 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. (Seit R2024b)
- SCHRITT 1: Define Requirements for Battery State of Charge Estimation
- SCHRITT 2: Prepare Data for Battery State of Charge Estimation Using Deep Learning
- SCHRITT 3: Train Deep Learning Network for Battery State of Charge Estimation
- SCHRITT 4: Compress Deep Learning Network for Battery State of Charge Estimation
- SCHRITT 5: Test Deep Learning Network for Battery State of Charge Estimation
- SCHRITT 6: Integrate AI Model into Simulink for Battery State of Charge Estimation
- SCHRITT 7: Generate Code for Battery State of Charge Estimation Using Deep Learning
- Train and Compress AI Model for Road Damage Detection
Train and compress a sequence classification network using pruning, projection, and quantization to meet a fixed memory requirement. (Seit R2025a)
- SCHRITT 1: Train Sequence Classification Network for Road Damage Detection
- SCHRITT 2: Compress Sequence Classification Network for Road Damage Detection
- SCHRITT 3: Tune Compression Parameters for Sequence Classification Network for Road Damage Detection
- SCHRITT 4: Generate Simulink Model from Sequence Classification Network for Road Damage Detection
- 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]. (Seit R2023b)
Enthaltene Beispiele
Field-Oriented Control of PMSM Using Position Estimated by Neural Network
Implement field-oriented control (FOC) of a permanent magnet synchronous motor (PMSM) using a rotor position estimated by an autoregressive neural network (ARNN) trained with Deep Learning Toolbox™.
