Journal
DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B
Volume -, Issue -, Pages -Publisher
AMER INST MATHEMATICAL SCIENCES-AIMS
DOI: 10.3934/dcdsb.2021248
Keywords
(mu, nu)-pseudo compact almost automorphic solution; global exponential stability; Clifford-valued neural network; mixed delay
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Funding
- National Natural Science Foundation of China [11861072]
- Applied Basic Research Foundation of Yunnan Province [2019FB003]
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In this study, a class of neutral type Clifford-valued cellular neural networks with discrete delays and infinitely distributed delays is considered. The existence and uniqueness of (mu, nu)-pseudo compact almost automorphic solutions of the networks are established, and the global exponential stability of the solution is investigated using differential inequality techniques. The theoretical findings are illustrated with an example and are shown to be novel even when the networks are degenerated into real-valued, complex-valued, or quaternion-valued networks.
We consider a class of neutral type Clifford-valued cellular neural networks with discrete delays and infinitely distributed delays. Unlike most previous studies on Clifford-valued neural networks, we assume that the self feedback connection weights of the networks are Clifford numbers rather than real numbers. In order to study the existence of (mu, nu)-pseudo compact almost automorphic solutions of the networks, we prove a composition theorem of (mu, nu)-pseudo compact almost automorphic functions with varying deviating arguments. Based on this composition theorem and the fixed point theorem, we establish the existence and the uniqueness of (mu, nu)-pseudo compact almost automorphic solutions of the networks. Then, we investigate the global exponential stability of the solution by employing differential inequality techniques. Finally, we give an example to illustrate our theoretical finding. Our results obtained in this paper are completely new, even when the considered networks are degenerated into real-valued, complex-valued or quaternion-valued networks.
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