A new class of neural networks and its applications

Abstract

We present a new class of neural network using a variable structure model of neuron (VSMN). From this structure, we generate four models of neurons. For each model, we study different behaviors such as stable or equilibrium, degraded, hole, alternated, oscillator, harmonic, fractal, and chaos behaviors. Then we design different topologies and architectures of neural networks. These architectures are different from the classical ones; each layer of network contains different models of neurons, neurons can take four models by configuration of VSMN. We also present a numerical study describing the behavior of some models of neuron. We illustrate some results to show the efficiency of this new class of neural networks. We show that these neurons put tracks on their stimulators such as: signal track and half bounded region track with two high and low directions. Two applications in chaos and robotic are also given.