Neural Network Toolbox™ provides functions and apps for modeling complex nonlinear systems that are not easily modeled with a closed-form equation. Neural Network Toolbox supports supervised learning with feedforward, radial basis, and dynamic networks. It also supports unsupervised learning with self-organizing maps and competitive layers. With the toolbox you can design, train, visualize, and simulate neural networks. You can use Neural Network Toolbox for applications such as data fitting, pattern recognition, clustering, time-series prediction, and dynamic system modeling and control.
To speed up training and handle large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox™.
Create a neural network to generalize nonlinear relationships between example inputs and outputs
Train a neural network to generalize from example inputs and their classes, construct a deep network using autoencoders
Discover natural distributions, categories, and category relationships
Model nonlinear dynamic systems; make predictions using sequential data
Control nonlinear systems using model-predictive, NARMA-L2, and model-reference neural networks
Define new neural network architectures and algorithms for advanced applications