Finite-time synchronization is a crucial phenomenon observed in nonlinear complex systems, the settling time in such a dynamic phenomenon is heavily depends on the initial states which may be unaccessible beforehand in the real world. Eliminating the dependence of the settling time on initial states leads to major advantage and convenience in practical applications. This paper is concerned with the fixed-/preassigned-time synchronization of delayed complex-valued neural networks(CVNNs) with discontinuous activations. By designing novel state feedback controllers, and with the help of Filippov regularization and inequality techniques, some new criteria for achieving fixed-/preassigned-time synchronization are established. The obtained theoretical results cover and supplement existing ones of the CVNNs with continuous activations. In addition, the upper-bound of the settling time is explicitly estimated. Finally, the validity of the theoretical results is supported by numerical simulations.
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http://dx.doi.org/10.1007/s11571-024-10129-6 | DOI Listing |
Cogn Neurodyn
October 2024
School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, 232001 Anhui People's Republic of China.
Finite-time synchronization is a crucial phenomenon observed in nonlinear complex systems, the settling time in such a dynamic phenomenon is heavily depends on the initial states which may be unaccessible beforehand in the real world. Eliminating the dependence of the settling time on initial states leads to major advantage and convenience in practical applications. This paper is concerned with the fixed-/preassigned-time synchronization of delayed complex-valued neural networks(CVNNs) with discontinuous activations.
View Article and Find Full Text PDFISA Trans
June 2023
College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China. Electronic address:
This paper is devoted to analyzing Fixed/Preassigned-time synchronization of T-S fuzzy complex networks (TSFCNs) with stochastic effects. Unlike the existing results, partial information communication and complete information communication are all considered according to a Bernoulli distribution. Furthermore, different controllers with quantization are structured to realize our synchronization goal, and one of control parameters can switch based on the error information.
View Article and Find Full Text PDFISA Trans
May 2023
College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, China.
This paper is concentrated on the fixed/preassigned-time (FXT/PAT) synchronization of multilayered networks, in which the self-dynamics of nodes are heterogeneous and the synchronized state can be an arbitrary prescribed smooth orbit. Above all, the original network is augmented by involving the synchronized state as a virtual node, it is allowed to remove the topological connectivity limitations and reduce the conservatism of the synchronization conditions. Subsequently, several continuous control protocols have been developed to achieve FXT synchronization and some effective criteria are established by utilizing the theorem of FXT stability.
View Article and Find Full Text PDFNeural Netw
February 2022
College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, China. Electronic address:
The fixed-time synchronization and preassigned-time synchronization of quaternion-valued neural networks are concerned in this article. By developing fixed-time stability and proposing a pure power-law control scheme, some simple conditions are obtained to realize fixed-time synchronization of quaternion-valued neural networks and the upper bound of the synchronized time is provided. Furthermore, the preassigned-time synchronization of quaternion-valued neural networks is investigated based on pure power-law control design, where the synchronization time is preassigned in advance and the control gains are finite.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
October 2022
This article concerns the problems of synchronization in a fixed time or prespecified time for memristive complex-valued neural networks (MCVNNs), in which the state variables, activation functions, rates of neuron self-inhibition, neural connection memristive weights, and external inputs are all assumed to be complex-valued. First, the more comprehensive fixed-time stability theorem and more accurate estimations on settling time (ST) are systematically established by using the comparison principle. Second, by introducing different norms of complex numbers instead of decomposing the complex-valued system into real and imaginary parts, we successfully design several simpler discontinuous controllers to acquire much improved fixed-time synchronization (FXTS) results.
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