¶ 相关文件：NeuralNetworks
Implementation of Neural Networks in a Pulsar Controller might be possible through Sockets. The Neural Networks Toolbox in Matlab is supported in Simulink and is (in theory!) supported in Sockets. This is the Matlab literature on Neural Net’s in Simulink:
Once a network has been created and trained, it can be easily incorporated into Simulink models. A simple command (gensim) automatically generates network simulation blocks for use with Simulink. This feature also makes it possible for you to view your networks graphically.
Neural network simulation blocks for use in Simulink can be automatically generated using the gensim command. Here, a three-layer neural network has been converted into Simulink blocks. Click on image to see enlarged view.
Control System Applications
Neural networks have been successfully applied 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.
Model Predictive Control Example
This example shows the model predictive control of a continuous stirred tank reactor (CSTR). This controller creates a neural network model of a nonlinear plant to predict future plant response to potential control signals. An optimization algorithm then computes the control signals that optimize future plant performance.
You can incorporate neural network predictive control blocks included in the toolbox into your existing Simulink models. By changing the parameters of these blocks you can tailor the network’s performance for your application.
This window displays a Simulink model that includes the neural network predictive control block and CSTR plant model (top left). Dialogs and panes allow you to visualize validation data (top right) and manage the neural network control block (lower left) and your plant identification (lower right). Click on image to see enlarged view.