Abstract
At present, the research on discrete-time Clifford-valued neural networks is rarely reported. However, the discrete-time neural networks are an important part of the neural network theory. Because the time scale theory can unify the study of discrete- and continuous-time problems, it is not necessary to separately study continuous- and discrete-time systems. Therefore, to simultaneously study the pseudo almost periodic oscillation and synchronization of continuous- and discrete-time Clifford-valued neural networks, in this paper, we consider a class of Clifford-valued fuzzy cellular neural networks on time scales. Based on the theory of calculus on time scales and the contraction fixed point theorem, we first establish the existence of pseudo almost periodic solutions of neural networks. Then, under the condition that the considered network has pseudo almost periodic solutions, by designing a novel state-feedback controller and using reduction to absurdity, we obtain that the drive-response structure of Clifford-valued fuzzy cellular neural networks on time scales with pseudo almost periodic coefficients can realize the global exponential synchronization. Finally, we give a numerical example to illustrate the feasibility of our results.
Highlights
Fuzzy cellular neural networks, introduced into the field of artificial neural networks in 1996 by Yang and Yang [1, 2], are a combination of fuzzy operations and cellular neural networks
The fuzzy cellular neural networks are widely used in the fields such as pattern recognition, computer science, artificial intelligence, optimal control, equation solving, robotics, military science, and so on
Because the application of neural networks in these fields is related to their long-term behaviors and the time delay is inevitable in real neural
Summary
Fuzzy cellular neural networks, introduced into the field of artificial neural networks in 1996 by Yang and Yang [1, 2], are a combination of fuzzy operations (fuzzy AND and fuzzy OR) and cellular neural networks They combine the advantages of neural network and fuzzy theory and integrate learning, association, recognition, and information processing. The fuzzy cellular neural networks are widely used in the fields such as pattern recognition, computer science, artificial intelligence, optimal control, equation solving, robotics, military science, and so on. Li and Shen Advances in Difference Equations (2020) 2020:593 networks, the dynamics of fuzzy cellular neural networks with various time delays has been extensively studied [3,4,5,6,7,8,9]. It is necessary and meaningful to study neural network models on time scales [10,11,12,13,14,15,16]
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