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Next, we will discuss Feed-Forward Neural Networks.
An FFNN is a network in which information is propagated only in one direction from the input to the output.
It has an input layer, an intermediate layer, and an output layer, each of which is composed of multiple nodes connected by edges.
The intermediate layer of 𝑘 can have multiple layers, and the learning of a network with a particularly deep intermediate layer is called deep learning.
In the intermediate layer, the linear layer and the activation function layer are alternately connected. Edges have "weights" and the value of each node is calculated from the value of the node in the previous layer connected to it.
That is, it is computed from the values of the nodes in the previous layer, the weights of the connected edges, and the activation function of the layer.
The 〖𝒘_𝑚〗^H 𝒍[𝑘] in equation (6) of the fast immovable point algorithm can be represented as a linear layer since it performs linear operations.
Equation (7) of the target orthogonalization is a nonlinear operation, and the fast immovable point algorithm is a nonlinear operation by incorporating the learnable parameter 𝛼 in the exponential part for equation (5-3), which can be represented as the activation function layer. |
IEEE/ICACT20230070 Slide.15
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