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Decomposition method-based global Mittag-Leffler synchronization for fractional-order Clifford-valued neural networks
R Sriraman1, N Manoj1, Anbalagan Pratap2
1Department of Mathematics, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India.
Abstract:
This study examines the global Mittag-Leffler synchronization (GMLS) problem for fractional-order Clifford-valued neural networks (FOCLVNNs) including transmission delays and impulses. Firstly, a novel kind of FOCLVNNs is developed that incorporates impulses and transmission delays, which can be useful for better understanding of Clifford-valued neural networks (CLVNNs) dynamics more precisely. Then, because of the non-commutative multiplication feature of the Clifford algebra, the systems of FOCLVNNs can be decomposed into multi-dimensions real-valued neural networks (RVNNs), that avoid the difficulties associated with Clifford number multiplication. By using Lyapunov functions and inequality techniques, several novel solid conditions for the GMLS of FOCLVNNs under the state feedback controller that has been formulated. In addition, this paper presents novel results that are easy to solve and new insights into GMLS of FOCLVNNs. Then, a numerical verification is given along with graphical analysis to validate the applicability of the obtained theories. Based on the considered master-slave FOCLVNNs, an image encryption algorithm for color images is provided. In addition, simulation and performance analysis are stated to prove the validity of the given algorithms.
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