Neural Network Toolbox

Introduction

Neural Network Toolbox™ provides tools for designing, implementing, visualizing, and simulating neural networks. Neural networks are used for applications where formal analysis would be difficult or impossible, such as pattern recognition and nonlinear system identification and control. The toolbox supports feedforward networks, radial basis networks, dynamic networks, self-organizing maps, and other proven network paradigms.

Getting Started with Neural Network Toolbox 4:20
Use graphical tools to apply neural networks to data fitting, pattern recognition, clustering, and time series problems.

Key Features

• Neural network design, training, and simulation
• Pattern recognition, clustering, and data-fitting tools
• Supervised networks including feedforward, radial basis, LVQ, time delay, nonlinear autoregressive (NARX), and layer-recurrent
• Unsupervised networks including self-organizing maps and competitive layers
• 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
• Routines for improving generalization to prevent overfitting
• Simulink^® blocks for building and evaluating neural networks, and advanced blocks for control systems applications