$$\mathscr {H}_{\infty }$$ Control for the Stabilization of Neural Networks with Time-Varying Delay

Ju H. Park (), Tae H. Lee, Yajuan Liu and Jun Chen
Additional contact information
Ju H. Park: Yeungnam University, Department of Electrical Engineering
Tae H. Lee: Chonbuk National University, Division of Electronic Engineering
Yajuan Liu: North China Electric Power University, Control and Computer Engineering
Jun Chen: Jiangsu Normal University, School of Electrical Engineering and Automation

Chapter 7 in Dynamic Systems with Time Delays: Stability and Control, 2019, pp 179-198 from Springer

Abstract: Abstract This chapter is concerned with the $$\mathscr {H}_{\infty }$$ control problem for neural networks with time-varying delays and external disturbance. To derive less conservative controller design conditions, the time-varying delay interval divided into two subintervals in which the weighting factor $$\alpha $$ is introduced to make nonequal subintervals. By Lyapunov stability theory, we derive linear matrix inequalities for designing the controller and show the effectiveness of them by a numerical example.

Keywords: Neural networks; $$\mathscr {H}_{\infty }$$ control; Time-varying delay; External disturbance (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-13-9254-2_7

Ordering information: This item can be ordered from
http://www.springer.com/9789811392542

DOI: 10.1007/978-981-13-9254-2_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().