Fractional Stochastic Search Algorithms: Modelling Complex Systems via AI

Abstract

The aim of this article is to establish a stochastic search algorithm for neural networks based on the fractional stochastic processes {BtH,t≥0} with the Hurst parameter H∈(0,1). We define and discuss the properties of fractional stochastic processes, {BtH,t≥0}, which generalize a standard Brownian motion. Fractional stochastic processes capture useful yet different properties in order to simulate real-world phenomena. This approach provides new insights to stochastic gradient descent (SGD) algorithms in machine learning. We exhibit convergence properties for fractional stochastic processes.

Keywords

• fractional Brownian motion
• fractional stochastic gradient descent
• machine learning
• stochastic gradient descent
• complex systems