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

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-tim...

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Vydané v:Chaos, solitons and fractals Ročník 119; s. 269 - 275
Hlavní autori: Castañeda, Carlos E., López-Mancilla, D., Chiu, R., Villafaña-Rauda, E., Orozco-López, Onofre, Casillas-Rodríguez, F., Sevilla-Escoboza, R.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.02.2019
Predmet:
Arduino UNO microcontroller
Chaos synchronization
Embedded systems
Genesio & Tesi chaotic system
MicroLabBox
Recurrent neural networks
Recurrent neural networks
Embedded systems
Chaos synchronization
MicroLabBox
Arduino UNO microcontroller
Genesio & Tesi chaotic system
ISSN:0960-0779, 1873-2887
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Shrnutí: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.
ISSN:0960-0779
1873-2887
DOI:10.1016/j.chaos.2018.12.030