Parallel approach to the Levenberg-marquardt learning algorithm for feedforward neural networks

Abstract

A parallel architecture of the Levenberg-Marquardt algorithm for training a feedforward neural network is presented. The proposed solution is based on completely new parallel structures to effectively reduce high computational load of this algorithm. Detailed parallel neural network structures are explicitely discussed.