Title: Disparate synchronisation phenomena in diffusively coupled SC-CNN-based MLCV circuits: analytical, numerical and experimental

Authors: H. Shameem Banu; P.S. Sheik Uduman; Kathamuthu Thamilmaran

Addresses: Department of Physics, B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, India ' Department of Mathematics & AS, B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, India ' Center for Nonlinear Systems, Chennai Institute of Technology, Chennai, Tamilnadu 600 069, India

Abstract: In this paper, the disparate synchronisation phenomena investigated in a system of two diffusively coupled forced parallel LCR circuits in the state controlled-cellular neural network (SC-CNN) model, were studied. In this aspect, the system transit from the unsynchronised state to the phenomena of phase synchronisation, lag synchronisation, and a complete synchronisation state regime, are the observed synchronisation phenomena, as the coupling parameter is varied. They are characterised by phase portraits and time series from an unsynchronised state to the complete synchronised state. As a result, we illustrate the synchronised states of the proposed model system solved analytically using an explicit analytical solution, numerical simulation, and confirmed with the real-time hardware experimental results are presented. Also, confirmed by the characteristics of Poincare map for phase synchronisation and Similarity function for lag synchronisation, using the relevant data. The study of these dynamic arrays of such bidirectionally coupled SC-CNN circuit systems will be valuable to understand image processing and other neural dynamics.

Keywords: state controlled-cellular neural network; SC-CNN; bidirectional coupling; Murali-Lakshmanan-Chua's; MLCV; circuit; lag synchronisation; LS; phase synchronisation; PS; complete synchronisation.

DOI: 10.1504/IJNDC.2022.124208

International Journal of Nonlinear Dynamics and Control, 2022 Vol.2 No.2, pp.116 - 132

Received: 14 Nov 2021
Accepted: 20 Feb 2022
Published online: 18 Jul 2022 *

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