On The Universality of The Single-Layer Perceptron Model

Abstract

The Single Layer Perceptron (SLP) calculates a weighted sum of numerousinputs and produces output as a smooth non-linear two side boundedfunction of this sum. We show that this simple mathematical model,originally proposed to consider the information processing in braincells, features much more universal principles. If appropriatelytrained, the SLP can implement many commonly known statisticalclassification and regression algorithms. The SLP training andretraining can be used to model aging processes in technology, biologyand society. The SLP model can be utilized to simulate continuous andturbulent wave propagation in excitable medias.