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Synchronization and FPGA realization of complex networks with fractional–order Liu chaotic oscillators

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  • A.G., Soriano–Sánchez
  • C., Posadas–Castillo
  • M.A., Platas–Garza
  • A., Arellano–Delgado

Abstract

In this paper synchronization of fractional–order Liu chaotic oscillators is addressed. Two networks of fractional–order Liu chaotic oscillators are synchronized by appealing to results from complex systems theory for integer–order systems. We use and implement complex dynamical networks composed by nine Liu chaotic oscillators. Two scenarios are considered: (i) complex network with regular topology, and (ii) complex network with irregular topology. Synchronization of both complex networks is achieved by coupling the fractional–order chaotic oscillators through their second state. We use numerical simulations to verify the results, we also show an FPGA realization of the complex networks previously synchronized.

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  • A.G., Soriano–Sánchez & C., Posadas–Castillo & M.A., Platas–Garza & A., Arellano–Delgado, 2018. "Synchronization and FPGA realization of complex networks with fractional–order Liu chaotic oscillators," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 250-262.
    • Handle: RePEc:eee:apmaco:v:332:y:2018:i:c:p:250-262
    DOI: 10.1016/j.amc.2018.03.057
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    Cited by:

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    2. Sun, Bo & Cao, Yuting & Guo, Zhenyuan & Yan, Zheng & Wen, Shiping, 2020. "Synchronization of discrete-time recurrent neural networks with time-varying delays via quantized sliding mode control," Applied Mathematics and Computation, Elsevier, vol. 375(C).
    3. Abdulaziz S. Alkabaa & Osman Taylan & Mustafa Tahsin Yilmaz & Ehsan Nazemi & El Mostafa Kalmoun, 2022. "An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach," Mathematics, MDPI, vol. 10(4), pages 1-21, February.

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