Evolutionary Identification of Active Particle Systems

Abstract

This paper presents how it is possible to introduce active motricity into particle-bond systems used in applications such as image animation. We chose to add into some neural network capabilities over the classical approach, in order to obtain a system able to model a larger class of behaviour. Therefore a new type of binary bond enriched with a neural-based command ability is proposed and tested in this paper. This “active” bond acts like a controlled muscle in order to produce motricity. An Evolutionary Strategy is used to optimise the particle-bond system parameters through evolving parameter sets. We tested our method both on artificially generated data and on data collected from real-life motion. Results and comparisons between our method and other approaches show the advantage of using active particle-bond systems for image animation applications.