Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6865766 | Neurocomputing | 2015 | 23 Pages |
Abstract
This paper focuses on the state estimation problem for a class of discrete-time switching neural networks with persistent dwell time (PDT) switching regularities and mode-dependent time-varying delays in Hâ sense. The considered switching regularity is more general that extends the frequently studied dwell-time (DT) and average dwell-time (ADT) switching. The random packet dropouts, which are governed by a Bernoulli distributed white sequence, are considered to exist together for the estimator design of underlying switching neural networks. The desired mode-dependent estimators are designed such that the resulting estimation error system is exponentially mean-square stable and achieves a prescribed Hâ level of disturbance attenuation. Finally, the effectiveness and the superiority of the developed results are demonstrated through a class of synthetic oscillatory networks.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yanzheng Zhu, Lixian Zhang, Zepeng Ning, Zhenzong Zhu, Wafa Shammakh, Tasawar Hayat,