Multilayer Shallow Neural Networks and Backpropagation Training
The shallow multilayer feedforward neural network 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.
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:
Create the network
Configure the network
Initialize the weights and biases
Train the network
Validate the network (post-training analysis)
Use the network
Step 1 might happen outside the framework of Deep Learning Toolbox™ software, but this step is critical to the success of the design process.
Details of this workflow are discussed in these sections:
Create, Configure, and Initialize Multilayer Shallow Neural Networks
Optional workflow steps are discussed in these sections:
For time series, dynamic modeling, and prediction, see this section: