A review of the design and implementation of the proposed model for an automatic link generation device with neural network correlation

In this article, the modeling of an automatic link generation device or ALE using neural networks or artificial intelligence is introduced.

In general, the performance of an ALE device can be modeled.

The goal of modeling is to design this device using new methods and implement it using existing tools and, as a result, to make it local.

In this regard, this paper attempts to model and implement the overall performance of the ALE device using neural networks and the MLP algorithm.

First, after examining the communication channels and the effects of nonlinearity and noise on data transmission, a model for data transmission in communication channels is introduced and coded in the required software environment. Then, the types of neural networks and their applications are introduced and the best algorithm for modeling the ALE device is selected.

In the following, several models are implemented and compared using the required software tools and MLP algorithm coding.

Finally, the proposed model based on the MLP algorithm can predict the appropriate channel with the least error, instead of a new output. The proposed models, after optimization, can be implemented on FPGA and provide a way to build this device within the country.