Quantized H∞ stabilization for delayed memristive neural networks

• Published in: Neural computing & applications Vol. 35; no. 22; pp. 16473 - 16486
• Main Authors: Yan, Zhilian, Zuo, Dandan, Guo, Tong, Zhou, Jianping
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2023; Springer Nature B.V
• Subjects: Artificial Intelligence; Closed loop systems; Closed loops; Communication; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Control systems design; Controllers; Data Mining and Knowledge Discovery; Dynamic models; Euclidean space; Feedback control; H-infinity control; Image Processing and Computer Vision; Neural networks; Original Article; Probability and Statistics in Computer Science; Stabilization; Time dependence; Time lag; Sampled-data control; Memristive neural networks; control; Dynamic quantizer

The issue of H ∞ stabilization for delayed memristive neural networks with dynamic quantization is considered. The aim is to design a quantized sampled-data controller guaranteeing that the closed-loop system is globally asymptotically stable with a prescribed H ∞ disturbance attenuation level. By m...