A new back-propagation algorithm with coupled neuron

Abstract

A novel neuron model and its learning algorithm are presented. They provide a novel approach for speeding up convergence in the learning of layered neural networks and for training networks of neurons with a nondifferentiable output function by using the gradient descent method. The neuron is called a saturating linear coupled neuron (sl-CONE). From simulation results, it is shown that the sl-CONE has a high convergence rate in learning compared with the conventional backpropagation algorithm.