Introduction to Dynamic Neural Networks

Neural networks can be classified into dynamic and static categories. Static (feedforward) networks have no feedback elements and contain no delays; the output is calculated directly from the input through feedforward connections. In dynamic networks, the output depends not only on the current input to the network, but also on the current or previous inputs, outputs, or states of the network.

Details of this workflow are discussed in the following sections:

• Design Time Series Time-Delay Neural Networks

• Prepare Input and Layer Delay States

• Design Time Series Distributed Delay Neural Networks

• Design Time Series NARX Feedback Neural Networks

• Design Layer-Recurrent Neural Networks

Optional workflow steps are discussed in these sections:

• Choose Neural Network Input-Output Processing Functions

• Divide Data for Optimal Neural Network Training

• Train Neural Networks with Error Weights