Secure communication holds excellent significance in science and technology, and a crucial aspect of achieving it lies in synchronizing chaotic systems. Indeed, finite-time synchronization (FTS) can ensure that the transmitter and receiver systems achieve synchronization rapidly within a finite time, enhancing the security and efficiency of the communication process. The primary objective of this project is to explore the practical implementation of FTS techniques for chaotic fractional-order neural networks (FONNs) and their relevance in analyzing image encryption/decryption problems. Such investigations will be considered for several classes of chaotic FONNs, such as switching cases, complex-valued, and cellular models; they will recall the more complex chaotic sequences based on their characteristics. Specifically, these investigations will use a finite-time domain approach to obtain sufficient criteria for synchronizing chaotic FONN models. Based on these theoretical findings, the chaotic sequence will be applied to improve the chaotic-based encryption scheme to encrypt the image for secure communication. The project’s goal will be to demonstrate the effectiveness of the proposed approach in achieving secure image transmission via numerical simulations.
Project funding:
Research Council of Lithuania (RCL), Projects of Postdoctoral fellowships funded by the state budget of the Republic of Lithuania
Project results:
This work theoretically explores the coexistence of synchronization and state estimation analysis through output sampling measures for a class of memristive neural networks operating within the flux-charge domain. These networks are subject to constant delayed responses in self-feedback loops and time-varying delayed responses incorporated into the activation functions. A contemporary output sampling controller is designed to discretize system dynamics based on available output measurements, which enhances control performance by minimizing update frequency, thus overcoming network bandwidth limitations and addressing network synchronization and state vector estimation. By utilizing differential inclusion mapping to capture weights from discontinuous memristive switching actions and an input-delay approach to bound nonuniform sampling intervals, we present linear matrix inequality-based sufficient conditions for synchronization and vector estimation criteria under the Lyapunov–Krasovskii functional framework and relaxed integral inequality. Finally, by utilizing the preset experimental data-set, we visually verify the adaptability of the proposed theoretical findings concerning synchronization, anti-synchronization, and vector state estimation of delayed memristive neural networks operating in the flux-charge domain. Furthermore, numerical validation through simulation demonstrates the impact of leakage delay and output measurement sampling by comparative analysis with scenarios lacking leakage and sampling measurements.
Project documents:
G.Soundararajan, R.Suvetha, M.Ragulskis, P.Prakash. Output sampling synchronization and state estimation in flux-charge domain memristive neural networks with leakage and time-varying delay. Neural Networks. ISSN 0893-6080, Elsevier, 2025, vol.184, article no.107018. | 107018 |
Period of project implementation: 2024-04-02 - 2026-04-01
Project coordinator: Kaunas University of Technology
