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Approximation de fonction et régression non linéaire
Applications
Neural Net Fitting | Résoudre un problème d’ajustement avec des réseaux feedforward à deux couches |
Fonctions
fitnet | Réseau de neurones pour l'ajustement de fonction |
feedforwardnet | Générer un réseau de neurones feedforward |
cascadeforwardnet | Generate cascade-forward neural network |
train | Entraîner un réseau de neurones peu profond |
trainlm | Levenberg-Marquardt backpropagation |
trainbr | Bayesian regularization backpropagation |
trainscg | Scaled conjugate gradient backpropagation |
trainrp | Resilient backpropagation |
mse | Fonction de performance d’erreur quadratique moyenne normalisée |
regression | (Not recommended) Perform linear regression of shallow network outputs on targets |
ploterrhist | Plot error histogram |
plotfit | Tracer l'approximation d'une fonction |
plotperform | Tracer les performances d’un réseau |
plotregression | Tracer une régression linéaire |
plottrainstate | Tracer les valeurs d'un état de l’apprentissage |
genFunction | Generate MATLAB function for simulating shallow neural network |
Exemples et procédures
Design de base
- Ajuster des données avec un réseau de neurones peu profond
Entraînez un réseau de neurones peu profond à ajuster des jeux de données. - Create, Configure, and Initialize Multilayer Shallow Neural Networks
Prepare a multilayer shallow neural network. - Body Fat Estimation
This example illustrates how a function fitting neural network can estimate body fat percentage based on anatomical measurements. - Train and Apply Multilayer Shallow Neural Networks
Train and use a multilayer shallow network for function approximation or pattern recognition. - Analyze Shallow Neural Network Performance After Training
Analyze network performance and adjust training process, network architecture, or data. - Deploy Shallow Neural Network Functions
Simulate and deploy trained shallow neural networks using MATLAB® tools. - Deploy Training of Shallow Neural Networks
Learn how to deploy training of shallow neural networks.
Scalabilité et efficacité de l’apprentissage
- Shallow Neural Networks with Parallel and GPU Computing
Use parallel and distributed computing to speed up neural network training and simulation and handle large data. - Automatically Save Checkpoints During Neural Network Training
Save intermediate results to protect the value of long training runs. - Optimize Neural Network Training Speed and Memory
Make neural network training more efficient.
Solutions optimales
- Choose Neural Network Input-Output Processing Functions
Preprocess inputs and targets for more efficient training. - Configure Shallow Neural Network Inputs and Outputs
Learn how to manually configure the network before training using theconfigure
function. - Divide Data for Optimal Neural Network Training
Use functions to divide the data into training, validation, and test sets. - Choose a Multilayer Neural Network Training Function
Comparison of training algorithms on different problem types. - Improve Shallow Neural Network Generalization and Avoid Overfitting
Learn methods to improve generalization and prevent overfitting. - Train Neural Networks with Error Weights
Learn how to use error weighting when training neural networks. - Normalize Errors of Multiple Outputs
Learn how to fit output elements with different ranges of values.
Concepts
- Workflow for Neural Network Design
Learn the primary steps in a neural network design process.
- Four Levels of Neural Network Design
Learn the different levels of using neural network functionality.
- Réseaux de neurones peu profonds multicouches et apprentissage par rétropropagation
Workflow pour le design d’un réseau de neurones feedforward peu profond multicouche pour l'ajustement de fonction et la reconnaissance de formes.
- Multilayer Shallow Neural Network Architecture
Learn the architecture of a multilayer shallow neural network.
- Understanding Shallow Network Data Structures
Learn how the format of input data structures affects the simulation of networks.
- Exemples de jeux de données pour les réseaux de neurones peu profonds
Liste d’exemples de jeux de données à utiliser pour s’entraîner sur les réseaux de neurones peu profonds.
- Neural Network Object Properties
Learn properties that define the basic features of a network.
- Neural Network Subobject Properties
Learn properties that define network details such as inputs, layers, outputs, targets, biases, and weights.