Probability-guaranteed encoding–decoding-based state estimation for delayed memristive neutral networks with event-triggered mechanism

IF 3.9 4区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-05-07 DOI:10.1002/acs.3831

Chen Hu, Shuhua Zhang, Hongyuan Zhao, Lifeng Ma, Jian Guo

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引用次数: 0

Abstract

This article handles the probability-guaranteed state estimation problem for a class of nonlinear memristive neural networks (MNNs) by using an event-triggered mechanism. Both time-varying delays and incomplete measurements are considered in the MNNs dynamics. To mitigate the impact of limited communication bandwidth, a communication protocol is proposed that incorporates an encoding–decoding technique in addition to an event-triggered scheme. The aim is to devise a state estimator that can estimate the states of MNNs, ensuring that the state estimation error falls within the required ellipsoidal area with a desired chance. We obtain sufficient conditions for the feasibility of the addressed problem, where the requested gains can be found iteratively by solving certain convex optimization problems. On the basis of the proposed framework, some issues are further presented to determine locally optimal estimator parameters according to different specifications. Finally, we utilize an illustrative numerical example to show the validity of our provided theoretical results.

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具有事件触发机制的延迟记忆中性网络基于概率保证的编码-解码状态估计

摘要本文利用事件触发机制处理一类非线性记忆神经网络（MNN）的概率保证状态估计问题。在 MNNs 动态中考虑了时变延迟和不完全测量。为了减轻有限通信带宽的影响，除了事件触发方案外，还提出了一种结合了编码-解码技术的通信协议。我们的目标是设计一种能够估计多节点网络状态的状态估计器，确保状态估计误差以理想的几率落在所需的椭圆形区域内。我们获得了所处理问题可行性的充分条件，其中所要求的增益可以通过迭代求解某些凸优化问题来找到。在提出的框架基础上，我们进一步提出了根据不同规格确定局部最优估计参数的一些问题。最后，我们利用一个数值示例来说明我们所提供的理论结果的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Adaptive Control and Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

16.10%

发文量

163

审稿时长

5 months

期刊介绍： The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.