Tunable reservoir computing based on iterative function systems

Abstract

In this study, a performance-tunable model of reservoir computing based on iterative function systems is proposed and its performance is investigated. Iterated function systems devised for fractal generation are applied to embody a reservoir for generating diverse responses for computation. Reservoir computing is a model of neuromorphic computation suitable for physical implementation owing to its easy feasibility. Flexibility in the parameter space of the iterated function systems allows the properties of the reservoir and the performance of reservoir computation to be tuned. Computer simulations reveal the features of the proposed reservoir computing model in a chaotic signal prediction problem. An experimental system was constructed to demonstrate an optical implementation of the proposed method.