The Function Representation of Artificial Neural Network
This addresses foundational issues in understanding and analyzing ANN structures for researchers in machine learning and AI, though it appears incremental as it builds on existing activation function concepts.
The paper tackles the problem of representing artificial neural network (ANN) structure by expressing it as a functional form using an activation integral concept, enabling mathematical solutions and placing ANN in a more reasonable framework.
This paper expresses the structure of artificial neural network (ANN) as a functional form, using the activation integral concept derived from the activation function. In this way, the structure of ANN can be represented by a simple function, and it is possible to find the mathematical solutions of ANN. Thus, it can be recognized that the current ANN can be placed in a more reasonable framework. Perhaps all questions about ANN will be eliminated.