Neural Network Toolbox

Simulink Blocks 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 the 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, examples, and Simulink blocks for three popular control applications:

  • Model predictive control, which uses a neural network model to predict future plant responses to potential control signals. An optimization algorithm then computes the control signals that optimize future plant performance. The neural network plant model is trained offline and in batch form.
  • Feedback linearization, which uses a rearrangement of the neural network plant model and is trained offline. This controller requires the least computation of these three architectures; however, the plant must either be in companion form or be capable of approximation by a companion form model.
  • Model reference adaptive control, which requires that a separate neural network controller be trained offline, in addition to the neural network plant model. While the controller training is computationally expensive, the model reference control applies to a larger class of plant than feedback linearization.

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.

Simulink model that uses the Neural Network Predictive Controller block with a tank reactor plant model.
A Simulink model that uses the Neural Network Predictive Controller block with a tank reactor plant model (top). You can visualize the dynamic response (bottom left) and manage the controller block (bottom center) and your plant identification (bottom right).
Next: Accelerated Training and Large Data Sets

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