Stability of Singular Discrete-Time Neural Networks With State-Dependent Coefficients and Run-to-Run Control Strategies. Academic Article

abstract

• In this brief, sustaining and intermittent run-to-run controllers are designed to achieve the stability of singular discrete-time neural networks with state-dependent coefficients. The controllers are designed for two reasons: 1) it is very difficult and almost impossible to only measure the in situ feedback information for the controllers and 2) the controllers may not always exist at any time. The stability is then established for singular discrete-time neural networks with state-dependent coefficients. Finally, numerical simulations are shown to illustrate the usefulness of the obtained criteria.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Neural Netw Learn Syst

author list (cited authors)

• Xiong, W., Yu, X., Patel, R., & Huang, T.

citation count

• 14

complete list of authors

• Xiong, Wenjun||Yu, Xinghuo||Patel, Ragini||Huang, Tingwen

publication date

• December 2018

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• n2162-237XISSN  Journal

keywords

• Intermittent Run-to-run Controllers
• Singular Discrete-time Neural Networks
• State-dependent Coefficients
• Sustaining Run-to-run Controllers

Digital Object Identifier (DOI)

• 10.1109/TNNLS.2018.2829172

start page

• 6415

end page

• 6420

volume

• 29

issue

• 12

URL

• http://dx.doi.org/10.1109/tnnls.2018.2829172