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1D and 2D Chaotic Time Series Prediction Using Hierarchical Reservoir Computing System

Md Razuan Hossain

Electrical and Computer Engineering Department, University of Mississippi, Oxford, Mississippi, USA

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Anurag Dhungel

Electrical and Computer Engineering Department, University of Mississippi, Oxford, Mississippi, USA

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Maisha Sadia

Electrical and Computer Engineering Department, University of Mississippi, Oxford, Mississippi, USA

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Partha Sarathi Paul

Electrical and Computer Engineering Department, University of Mississippi, Oxford, Mississippi, USA

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, and

Md Sakib Hasan

Electrical and Computer Engineering Department, University of Mississippi, Oxford, Mississippi, USA

E-mail Address: mhasan5@olemiss.edu

Corresponding author.

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https://doi.org/10.1142/S0129156423500143Cited by:1 (Source: Crossref)

This article is part of the issue:

Special Issue on Nanotechnology in Electronics, Photonics, Biosensors, and Energy Systems
Guest Editors: F. Jain, C. Broadbridge, M. Gherasimova and H. Tang

Abstract

Reservoir Computing (RC) is a type of machine learning inspired by neural processes, which excels at handling complex and time-dependent data while maintaining low training costs. RC systems generate diverse reservoir states by extracting features from raw input and projecting them into a high-dimensional space. One key advantage of RC networks is that only the readout layer needs training, reducing overall training expenses. Memristors have gained popularity due to their similarities to biological synapses and compatibility with hardware implementation using various devices and systems. Chaotic events, which are highly sensitive to initial conditions, undergo drastic changes with minor adjustments. Cascade chaotic maps, in particular, possess greater chaotic properties, making them difficult to predict with memoryless devices. This study aims to predict 1D and 2D cascade chaotic time series using a memristor-based hierarchical RC system.

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