Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
408447 | Neurocomputing | 2011 | 5 Pages |
Abstract
The state estimation problem is studied in this paper for a class of recurrent neural networks with time-varying delay. A novel delay partition approach is developed to derive a delay-dependent condition guaranteeing the existence of a desired state estimator for the delayed neural networks. The design of the gain matrix of the state estimator can be achieved by solving a linear matrix inequality, where no slack variable is involved. A numerical example is finally provided to show the advantage of the proposed approach over some existing results.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
He Huang, Gang Feng,