Reduced-order state estimation of delayed recurrent neural networks. Academic Article

abstract

• Different from the widely-studied full-order state estimator design, this paper focuses on dealing with the reduced-order state estimation problem for delayed recurrent neural networks. By employing an integral inequality, a delay-dependent design approach is proposed, and global asymptotical stability of the resulting error system is guaranteed. It is shown that the gain matrix of the reduced-order state estimator is determined by the solution of a linear matrix inequality. Numerical examples are provided to illustrate the effectiveness of the developed result.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

author list (cited authors)

• Huang, H. e., Huang, T., & Chen, X.

citation count

• 7

complete list of authors

• Huang, He||Huang, Tingwen||Chen, Xiaoping

publication date

• February 2018

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Global Asymptotical Stability
• Neural Networks, Computer
• Recurrent Neural Networks
• Reduced-order State Estimation
• Time Delay
• Time Factors

PubMed Central ID

• 29179153

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2017.11.002

start page

• 59

end page

• 64

volume

• 98

URL

• http://dx.doi.org/10.1016/j.neunet.2017.11.002