Briefly describe the structural classification of neural networks.

Briefly describe the structure classification of neural networks as follows:

1, feedforward neural network: This is the most common type of neural network in practical application. The first layer is input, and the last layer is output. If there are multiple hidden layers, we call it a "deep" neural network. They calculated a series of transformations that changed the similarity of samples. The activity of neurons in each layer is a nonlinear function of the activity in the previous layer.

2. Ring network: Ring networks determine the direction of circulation in their connection diagrams, which means that you can follow the arrow back to where you started. They may have complex dynamics, which makes training difficult. They are more biological.

The purpose of circular network is to process sequence data. In the traditional neural network model, from the input layer to the hidden layer and then to the output layer, all layers are fully connected, and the nodes between layers are not connected. But this common neural network is powerless to many problems. Cyclic neural network, that is, the current output of a sequence is also related to the previous output.

Specifically, the network will remember the previous information and apply it to the calculation of current output, that is, the nodes between hidden layers are no longer disconnected but connected, and the input of hidden layers includes not only the output of input layer, but also the output of hidden layer at the last moment.

3. Symmetrical connection network: Symmetrical connection network is a bit like a ring network, but the connections between units are symmetrical (they have the same weight in both directions). Symmetrically connected networks are easier to analyze than circular networks. There are more restrictions in this network because they obey the law of energy function.