首页> 外文期刊>Neurocomputing >Asynchronous finite-time state estimation for semi-Markovian jump neural networks with randomly occurred sensor nonlinearities
【24h】

Asynchronous finite-time state estimation for semi-Markovian jump neural networks with randomly occurred sensor nonlinearities

机译:随机发生传感器非线性的半马克洛维亚跳跃神经网络的异步有限时间估计

获取原文
获取原文并翻译 | 示例
           

摘要

This paper addresses the finite-time state estimation problem for semi-Markovian jump neural networks with sensor nonlinearities under the consideration of leakage delay and time-varying delay. The modes of original system and desired estimator are supposed to be asynchronous. Some sufficient conditions are proposed to guarantee the finite-time boundedness as well as mixed H-infinity and passive performance of the error system in terms of constructing Lyapunov-Krasovskii functionals. By utilizing affine Bessel-Legendre inequalities, a less conservative result can be achieved. By virtue of linear matrices inequalities approach, the asynchronous state estimator gains are obtained. Two numerical examples are provided to demonstrate the less conservativeness and effectiveness of our method. (C) 2020 Elsevier B.V. All rights reserved.
机译:本文在考虑泄漏延迟和时变延迟时,解决了具有传感器非线性的半马克洛维亚跳跃神经网络的有限时间状态估计问题。原始系统和所需估计的模式应该是异步的。提出了一些充分的条件,以确保在构建Lyapunov-Krasovskii功能方面保证有限时间的界限以及错误系统的混合H-Infinity和被动性能。通过利用仿射贝塞尔 - Legendre不等式,可以实现更少保守的结果。借助线性矩阵不等式方法,获得异步状态估计器增益。提供了两个数值例子以证明我们方法的保守性和有效性。 (c)2020 Elsevier B.v.保留所有权利。

著录项

获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号