A multilayer neural network with piecewise-linear structure and back-propagation learning

Abstract

A multilayer neural network which is given a two-layer piecewise-linear structure for every cascaded section is proposed. The neural networks have nonlinear elements that are neither sigmoidal nor of a signum type. Each nonlinear element is an absolute value operator. It is almost everywhere differentiable, which makes back-propagation feasible in a digital setting. Both the feedforward signal propagation and the backward coefficient update rules belong to the class of regular iterative algorithms. This form of neural network specializes in functional approximation and is anticipated to have applications in control, communications, and pattern recognition.