Mixed-Delay-Dependent Augmented Functional for Synchronization of Uncertain Neutral-Type Neural Networks with Sampled-Data Control

Shuoting Wang and Kaibo Shi ()
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Shuoting Wang: School of Computer, Chengdu University, Chengdu 610106, China
Kaibo Shi: School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China

Mathematics, 2023, vol. 11, issue 4, 1-21

Abstract: In this paper, the synchronization problem of uncertain neutral-type neural networks (NTNNs) with sampled-data control is investigated. First, a mixed-delay-dependent augmented Lyapunov–Krasovskii functional (LKF) is proposed, which not only considers the interaction between transmission delay and communication delay, but also takes the interconnected relationship between neutral delay and transmission delay into consideration. Then, a two-sided looped functional is also involved in the LKF, which effectively utilizes the information on the intervals [ t k , t ] , [ t k − τ , t − τ ] , [ t , t k + 1 ) , [ t − τ , t k + 1 − τ ) . Furthermore, based on the suitable LKF and a free-matrix-based integral inequality, two synchronization criteria via a sampled-data controller considering communication delay are derived in forms of linear matrix inequalities (LMIs). Finally, three numerical examples are carried out to confirm the validity of the proposed criteria.

Keywords: neutral-type neural networks; synchronization problem; mixed-delay-dependent functional; sampled-data control; communication delay (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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