Division of Electronic Engineering, and Advanced Research Center of Electronics and Information, Chonbuk National University, Jeonju-Si 54896, South Korea.
Department of Mathematics, School of Natural Sciences, National Institute of Technology Calicut, Kozhikode, Kerala 673601, India.
Neural Netw. 2018 Oct;106:110-126. doi: 10.1016/j.neunet.2018.06.015. Epub 2018 Jul 5.
The issue of unified dissipativity-based Arcak-type state estimator design for delayed static neural networks (SNNs) with leakage term and noise distraction was considered here. An Arcak-type state observer, which is compact than the usually used Luenberger-type state estimator, is selected to implement the subject of a unified dissipativity performance of SNNs. This paper primarily concentrates on the issue of Arcak-type state estimator of delayed SNNs involving leakage delay. The first attempt is made to tackle the Arcak-type state estimator of SNNs with time delay in leakage term in this paper based on the unified criteria, by constructing a novel Lyapunov functional together with newly improved integral inequalities. As a result, a novel unified state estimation criterion is launched in the form of linear matrix inequalities (LMIs) and put forward to justify the dynamics of error system is extended dissipative with the influence of leakage term and estimator gain matrices K¯ and K¯. Finally, an interesting simulation study is ultimately explored to show the performance of the established unified dissipativity-based theoretical results, in which, comparison results are also made together with recent works as a special case.
本文考虑了具有时滞、泄露项和噪声干扰的静态神经网络（SNNs）的基于统一耗散的 Arcak 型状态估计器的设计问题。选择紧凑的 Arcak 型状态观测器来实现 SNNs 的统一耗散性能。本文主要集中在具有泄露时滞的延迟 SNNs 的 Arcak 型状态估计器问题上。本文首次基于统一准则，通过构造一个新的 Lyapunov 泛函和新的改进积分不等式，尝试解决带有泄露项时滞的 SNNs 的 Arcak 型状态估计器问题。结果，以线性矩阵不等式（LMIs）的形式提出了一个新的统一状态估计准则，并证明了在泄露项和估计器增益矩阵 K¯和 K¯的影响下，误差系统的动态是扩展耗散的。最后，进行了有趣的仿真研究，以展示所建立的基于统一耗散的理论结果的性能，其中还将结果与最近的工作作为特例进行了比较。
