Discrete-time neural synchronization between an Arduino microcontroller and a Compact Development System using multiscroll chaotic signals

Carlos E. Castañeda, D. López-Mancilla, R. Chiu, E. Villafaña-Rauda, Onofre Orozco-López, F. Casillas-Rodríguez and R. Sevilla-Escoboza

Chaos, Solitons & Fractals, 2019, vol. 119, issue C, 269-275

Abstract: In this paper, we present the synchronization of a chaotic system using a discrete-time recurrent high order neural network. This is done by using a Genesio & Tesi oscillator circuit in discrete-time embedded into an Arduino microcontroller that provides the state space variables. A discrete-time recurrent neural network is designed to synchronize the dynamics of the chaotic oscillator. This neural network is trained using a time-varying training algorithm where it is used the Extended Kalman Filter. Two state space variables are captured in real-time in ADC inputs of a compact development system, where these signals are synchronized by the recurrent high order neural network in discrete-time. The proposed work allows synchronization of interactions associated between the neural convergence and the chaotical plant state. The obtained real-time results, and the statistical analyses on the synchronization process validate the possible application in chaos-based communications systems.

Keywords: Chaos synchronization; Genesio & Tesi chaotic system; Recurrent neural networks; Arduino UNO microcontroller; MicroLabBox; Embedded systems (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:119:y:2019:i:c:p:269-275

DOI: 10.1016/j.chaos.2018.12.030

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