Adaptive Global Synchronization for a Class of Quaternion-Valued Cohen-Grossberg Neural Networks with Known or Unknown Parameters

Jun Guo (), Yanchao Shi, Weihua Luo, Yanzhao Cheng and Shengye Wang
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Jun Guo: College of Applied Mathematics, Chengdu University of Information Technology, Chengdu 610225, China
Yanchao Shi: School of Science, Southwest Petroleum University, Chengdu 610500, China
Weihua Luo: School of Mathematics and Physics, Hunan University of Arts and Science, Changde 415000, China
Yanzhao Cheng: School of Science, Southwest Petroleum University, Chengdu 610500, China
Shengye Wang: School of Science, Southwest Petroleum University, Chengdu 610500, China

Mathematics, 2023, vol. 11, issue 16, 1-16

Abstract: In this paper, the adaptive synchronization problem of quaternion-valued Cohen–Grossberg neural networks (QVCGNNs), with and without known parameters, is investigated. On the basis of constructing an appropriate Lyapunov function, and utilizing parameter identification theory and decomposition methods, two effective adaptive feedback schemes are proposed, to guarantee the realization of global synchronization of CGQVNNs. The control gain of the above schemes can be obtained using the Matlab LMI toolbox. The theoretical results presented in this work enrich the literature exploring the adaptive synchronization problem of quaternion-valued neural networks (QVNNs). Finally, the reliability of the theoretical schemes derived in this work is shown in two interesting numerical examples.

Keywords: Cohen–Grossberg neural networks; quaternion; adaptive control; synchronization; linear matrix inequality (LMI) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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