Définir des architectures de réseaux de neurones peu profonds

Définir des architectures de réseaux de neurones peu profonds et des algorithmes

Fonctions

network Créer un réseau de neurones peu profond personnalisé

Exemples et procédures

Réseaux de neurones personnalisés

Create Neural Network Object
Create and learn the basic components of a neural network object.
Configure Shallow Neural Network Inputs and Outputs
Learn how to manually configure the network before training using the configure function.
Understanding Shallow Network Data Structures
Learn how the format of input data structures affects the simulation of networks.
Edit Shallow Neural Network Properties
Customize network architecture using its properties and use and train the custom network.

Réseaux de neurones historiques et alternatifs

Adaptive Neural Network Filters
Design an adaptive linear system that responds to changes in its environment as it is operating.
Perceptron Neural Networks
Learn the architecture, design, and training of perceptron networks for simple classification problems.
Classification with a Two-Input Perceptron
A two-input hard limit neuron is trained to classify four input vectors into two categories.
Outlier Input Vectors
A 2-input hard limit neuron is trained to classify 5 input vectors into two categories.
Normalized Perceptron Rule
A 2-input hard limit neuron is trained to classify 5 input vectors into two categories.
Linearly Non-separable Vectors
A 2-input hard limit neuron fails to properly classify 5 input vectors because they are linearly non-separable.
Radial Basis Neural Networks
Learn to design and use radial basis networks.
Radial Basis Approximation
This example uses the NEWRB function to create a radial basis network that approximates a function defined by a set of data points.
Radial Basis Underlapping Neurons
A radial basis network is trained to respond to specific inputs with target outputs.
Radial Basis Overlapping Neurons
A radial basis network is trained to respond to specific inputs with target outputs.
GRNN Function Approximation
This example uses functions NEWGRNN and SIM.
PNN Classification
This example uses functions NEWPNN and SIM.
Probabilistic Neural Networks
Use probabilistic neural networks for classification problems.
Generalized Regression Neural Networks
Learn to design a generalized regression neural network (GRNN) for function approximation.
Learning Vector Quantization (LVQ) Neural Networks
Create and train a Learning Vector Quantization (LVQ) Neural Network.
Learning Vector Quantization
An LVQ network is trained to classify input vectors according to given targets.
Linear Neural Networks
Design a linear network that, when presented with a set of given input vectors, produces outputs of corresponding target vectors.
Linear Prediction Design
This example illustrates how to design a linear neuron to predict the next value in a time series given the last five values.
Adaptive Linear Prediction
This example shows how an adaptive linear layer can learn to predict the next value in a signal, given the current and last four values.

Concepts

Workflow for Neural Network Design
Learn the primary steps in a neural network design process.
Neuron Model
Learn about a single-input neuron, the fundamental building block for neural networks.
Neural Network Architectures
Learn architecture of single- and multi-layer networks.