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Multilayer Neural Networks and Backpropagation Training

The multilayer feedforward neural network is the workhorse of the Neural Network Toolbox™ software. It can be used for both function fitting and pattern recognition problems. With the addition of a tapped delay line, it can also be used for prediction problems, as discussed in Design Time Series Time-Delay Neural Networks. This topic shows how you can use a multilayer network. It also illustrates the basic procedures for designing any neural network.

    Note   The training functions described in this topic are not limited to multilayer networks. They can be used to train arbitrary architectures (even custom networks), as long as their components are differentiable.

The work flow for the general neural network design process has seven primary steps:

  1. Collect data

  2. Create the network

  3. Configure the network

  4. Initialize the weights and biases

  5. Train the network

  6. Validate the network (post-training analysis)

  7. Use the network

Step 1 might happen outside the framework of Neural Network Toolbox software, but this step is critical to the success of the design process.

Details of this workflow are discussed in these sections:

Optional workflow steps are discussed in these sections:

For time series, dynamic modeling, and prediction, see this section:

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