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Circuits, Systems, and Signal Processing

New Results on Stability and Passivity for Discrete-Time Neural Networks with a Time-Varying Delay

  • 13-12-2024
Published in:
Authors
Hongjia Sha
Jun Chen
Guangming Zhuang
Published in
Circuits, Systems, and Signal Processing | Issue 4/2025

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Abstract

The article delves into the stability and passivity analysis of discrete-time neural networks with time-varying delays, a critical issue in various scientific and engineering fields. It introduces novel Lyapunov-Krasovskii functionals and delay-variation-dependent matrix inequalities to address the challenges posed by delays, which can lead to instability. By constructing a new augmented LKF that fully captures delay variation information, the authors reduce conservatism and simplify computations. The article also extends these findings to analyze the passivity of neural networks under external disturbances. Numerical examples demonstrate the effectiveness of the proposed methods, highlighting their advantages over existing approaches.
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previous article Further Results on the R–R Protocol Based Estimator for 2-D FMII Systems with R–O Uncertainties
next article Design and Implementation of a 16-bit Multi-mode 4-Channel Time-Interleaved Delta-Sigma Modulator with SNDR > 106 dB and DCE Compensation Based on FPGA
Title
New Results on Stability and Passivity for Discrete-Time Neural Networks with a Time-Varying Delay
Authors
Hongjia Sha
Jun Chen
Guangming Zhuang
Publication date
13-12-2024
Publisher
Springer US
Published in
Circuits, Systems, and Signal Processing / Issue 4/2025
Print ISSN: 0278-081X
Electronic ISSN: 1531-5878
DOI
https://doi.org/10.1007/s00034-024-02952-3
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