New Guaranteed H∞ Performance State Estimation for Delayed Neural Networks
Keywords:
State estimation, static neural networks, H-infinite performance, reciprocally convex approach, Timevarying delay.Abstract
In this paper, a new guaranteed performance state estimation problem for static neural networks with timevarying delay is investigated. A new Lyapunov-Krasovskii functional is introduced to improve the performance. Moreover, with the help of lower bound lemma, an upper-bound of a linear combination of positive functions weighted by the inverses of convex parameters is obtained. Two simulation examples are given to prove the effectiveness of the proposed theorem.
Downloads
Downloads
Published
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
