## Documentation |

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™.

Supervised networks, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and layer-recurrent

Unsupervised networks, including self-organizing maps and competitive layers

Apps for data-fitting, pattern recognition, and clustering

Parallel computing and GPU support for accelerating training (using Parallel Computing Toolbox)

Preprocessing and postprocessing for improving the efficiency of network training and assessing network performance

Modular network representation for managing and visualizing networks of arbitrary size

Simulink

^{®}blocks for building and evaluating neural networks and for control systems applications

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