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Volume 12 Issue 12
Dec.  2025

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  >  12(12): 2602-2611
T. Liu, S. Xie, Y. Xie, P. Liu, and T. Huang, “Predetermined-time output projective synchronization of coupled fuzzy neural networks via generalized exponential function,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 12, pp. 2602–2611, Dec. 2025. doi: 10.1109/JAS.2025.125519
Citation: T. Liu, S. Xie, Y. Xie, P. Liu, and T. Huang, “Predetermined-time output projective synchronization of coupled fuzzy neural networks via generalized exponential function,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 12, pp. 2602–2611, Dec. 2025. doi: 10.1109/JAS.2025.125519
shu

Predetermined-Time Output Projective Synchronization of Coupled Fuzzy Neural Networks via Generalized Exponential Function

doi: 10.1109/JAS.2025.125519
Funds:  This work was supported in part by the Distinguished Youth Foundation of Hunan Natural Science Foundation (2023JJ10079), the State Key Program of National Natural Science Foundation of China (62233018), and the National Natural Science Foundation of China (62373381)
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    Abstract
  • The main motivation of this paper arises from the fact that some complex systems have high demands for time precision, which need to reach the desired state in a pre-specified time interval. This paper addresses the predetermined-time output projective synchronization of coupled fuzzy neural networks. To mimic the uncertainty and relatedness among complex systems, coupled fuzzy neural networks are introduced to characterize complex systems in this paper. First, a novel controller is developed by means of a generalized exponential function and output states information, which can effectively avoid the chattering situations arising from the sign function. Under the controller, the output states of coupled fuzzy neural networks eventually converge to the projective state in the predefined time, which can reduce the requirements for sensor devices and improve the flexibility and efficiency of the control scheme. Second, in light of Lyapunov function and inequality techniques, sufficient criteria for ensuring to achieve the predetermined-time output projective synchronization of coupled fuzzy neural networks are deduced based on the assumption of the digraph containing a spanning tree. Furthermore, the results obtained in this paper not only represent an extension of master-slave systems but also demonstrate that the output synchronization of coupled fuzzy neural networks is a specific case of projective synchronization exemplified by a corollary. Finally, numerical examples are offered to reveal the correctness of theoretical results.

     

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