Further result on guaranteed H performance state estimation of delayed static neural networks. Academic Article

abstract

• This brief considers the guaranteed H performance state estimation problem of delayed static neural networks. An Arcak-type state estimator, which is more general than the widely adopted Luenberger-type one, is chosen to tackle this issue. A delay-dependent criterion is derived under which the estimation error system is globally asymptotically stable with a prescribed H performance. It is shown that the design of suitable gain matrices and the optimal performance index are accomplished by solving a convex optimization problem subject to two linear matrix inequalities. Compared with some previous results, much better performance is achieved by our approach, which is greatly benefited from introducing an additional gain matrix in the domain of activation function. An example is finally given to demonstrate the advantage of the developed result.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Neural Netw Learn Syst

author list (cited authors)

• Huang, H. e., Huang, T., & Chen, X.

citation count

• 86

complete list of authors

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

publication date

• June 2015

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Neural Networks and Learning Systems  Journal

keywords

• Algorithms
• Computer Simulation
• Neural Networks, Computer
• Regression Analysis
• Time Factors

Digital Object Identifier (DOI)

• 10.1109/TNNLS.2014.2334511

start page

• 1335

end page

• 1341

volume

• 26

issue

• 6

URL

• http://dx.doi.org/10.1109/tnnls.2014.2334511