Mapas autoorganizados
Apps
| Neural Net Clustering | Resolver un problema de agrupación utilizando redes de mapas autoorganizados (SOM) |
Funciones
selforgmap | Mapa autoorganizado |
train | Entrenar una red neuronal superficial |
plotsomhits | Representar los aciertos de muestra de un mapa autoorganizado |
plotsomnc | Representar conexiones vecinas de mapa autoorganizado |
plotsomnd | Representar distancias de vecinas de un mapa autoorganizado |
plotsomplanes | Representar planos de pesos de un mapa autoorganizado |
plotsompos | Representar posiciones de pesos de un mapa autoorganizado |
plotsomtop | Representar una topología de mapa autoorganizado |
genFunction | Generate MATLAB function for simulating shallow neural network |
Ejemplos y procedimientos
- Cluster Data with a Self-Organizing Map
Group data by similarity using the Neural Net Clustering app or command-line functions.
- 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.
- Iris Clustering
This example illustrates how a self-organizing map neural network can cluster iris flowers into classes topologically, providing insight into the types of flowers and a useful tool for further analysis.
- Gene Expression Analysis
This example demonstrates looking for patterns in gene expression profiles in baker's yeast using neural networks.
- One-Dimensional Self-Organizing Map
Neurons in a 2-D layer learn to represent different regions of the input space where input vectors occur.
- Two-Dimensional Self-Organizing Map
As in one-dimensional problems, this self-organizing map will learn to represent different regions of the input space where input vectors occur.
Conceptos
- Cluster with Self-Organizing Map Neural Network
Use self-organizing feature maps (SOFM) to classify input vectors according to how they are grouped in the input space.
