Neural Network Toolbox
Description
- Introduction and Key Features
- Working with Neural Network Toolbox
- Network Architectures
- Training and Learning Functions
- Preprocessing and Postprocessing Functions
- Improving Generalization
- Simulink Support and Control Systems Applications
Simulink Support and Control Systems Applications
Simulink Support
Neural Network Toolbox provides a set of blocks for building neural networks in Simulink. All blocks are compatible with Simulink Coder™. These blocks are divided into four 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.
Control Systems Applications
You can apply neural networks to the identification and control of nonlinear systems. The toolbox includes descriptions, demos, 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 uses the Neural Network Predictive Controller block with a tank reactor plant model (top). Dialog boxes and panes let you visualize validation data (bottom left) and manage the controller block (bottom center) and your plant identification (bottom right).
