Data classification using interferential neural network model

Abstract

Classical models of artificial neural networks have several disadvantages. To eliminate these shortcomings, a fundamentally new model of an artificial neural network, called the interferential model, is proposed. This model is based on the structure of biological neurons of the human brain. This work describes principles of work of interferential model. The results of the work show that the interferential model does not contain the disadvantages of classical neural networks. It is well suited for running classification task, as well as for pattern recognition.