Neural Network Toolbox 6.0.1

Product Description

• Introduction and Key Features
• Working with Neural Network Toolbox™
• Network Architectures
• Training and Learning Functions
• Simulink Support and Control System Applications
• Preprocessing and Postprocessing Functions

Simulink Support and Control System Applications

Simulink Support

Neural Network Toolbox provides a set of blocks for building neural networks in Simulink software. These blocks are divided into three libraries:

• Transfer function blocks, which take a net-input vector and generate a corresponding output vector
• Net input function blocks, which take any number of weighted input vectors, weight layer output vectors, and bias vectors, and return a net-input vector
• Weight function blocks, which apply a neuron's weight vector to an input vector (or a layer output vector) to get a weighted input value for a neuron
• Data preprocessing blocks, which map input and output data into ranges best suited for the neural network to handle directly.

• Alternatively, you can create and train your networks in the MATLAB environment and automatically generate network simulation blocks for use with Simulink. This approach also enables you to view your networks graphically.

A three-layer neural network converted into Simulink® blocks. Neural network simulation blocks for use in Simulink can be automatically generated with the gensim command. Click on image to see enlarged view.

Control System Applications

Neural Network Toolbox lets you apply neural networks to the identification and control of nonlinear systems. The toolbox includes descriptions, demonstrations, and Simulink blocks for three popular control applications: model predictive control, feedback linearization, and model reference adaptive control.

You can incorporate neural network predictive control blocks included in the toolbox into your Simulink models. By changing the parameters of these blocks, you can tailor the network's performance to your application.

A Simulink® model that includes the neural network predictive control block and CSTR plant model (top left). Dialogs and panes let you visualize validation data (lower left) and manage the neural network control block (lower right) and your plant identification (upper right). Click on image to see enlarged view.