Aproximación y regresión no lineal de funciones

Cree una red neuronal para generalizar relaciones no lineales entre entradas y salidas de ejemplo

Apps

Resolver un problema de ajuste utilizando redes prealimentadas de dos capas

`nftool`	Abrir la app Neural Net Fitting
`view`	Visualizar una red neuronal superficial

`fitnet`	Red neuronal de ajuste de funciones
`feedforwardnet`	Generar una red neuronal prealimentada
`cascadeforwardnet`	Generar una red neuronal prealimentada en cascada

`train`	Entrenar una red neuronal superficial
`trainlm`	Retropropagación Levenberg-Marquardt
`trainbr`	Retropropagación de regularización bayesiana
`trainscg`	Retropropagación de gradiente conjugado escalado
`trainrp`	Resilient backpropagation
`mse`	Función de rendimiento normalizada de error cuadrático medio
`regression`	(No recomendado) Realizar una regresión lineal de las salidas de redes superficiales en los objetivos
`ploterrhist`	Representar un histograma de error
`plotfit`	Representar el ajuste de una función
`plotperform`	Representar el rendimiento de la red
`plotregression`	Representar una regresión lineal
`plottrainstate`	Representar valores de estado de entrenamiento
`genFunction`	Generate MATLAB function for simulating shallow neural network

Fit Data with a Shallow Neural Network
Train a shallow neural network to fit a data set.
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.

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.

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 the configure 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.

Flujo de trabajo para el diseño de redes neuronales
Aprenda los principales pasos del proceso de diseño de una red neuronal.
Four Levels of Neural Network Design
Learn the different levels of using neural network functionality.
Redes neuronales superficiales multicapa y entrenamiento de retropropagación
Flujo de trabajo para diseñar una red neuronal prealimentada superficial multicapa para el ajuste de funciones y el reconocimiento de patrones.
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.
Conjuntos de datos de muestra para redes neuronales superficiales
Lista de conjuntos de datos de muestra que se pueden utilizar al experimentar con redes neuronales superficiales.
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.