Chapters authored
Pre-Informing Methods for ANNs
In the recent past, when computers just entered our lives, we could not even imagine what today would be like. If we look at the future with the same perspective today, only one assumption can be made about where technology will go in the near future; Artificial intelligence applications will be an indispensable part of our lives. While today’s work is promising, there is still a long way to go. The structures that researchers define as artificial intelligence today are actually programmed programs with limits and are result-oriented. Real learning includes many complex features such as convergence, association, inference and prediction. It has been demonstrated with an application how to transfer the input layer connections in human neurons to the artificial learning network with the pre-informing method. When the results are compared, the learning load (weights) was reduced from 147 to 9 with the proposed pre-informing method, and the learning rate was increased between 15–30% according to the activation function used.
Part of the book: Artificial Neural Networks