通过非还原阶次法对具有时变延迟的模糊惯性神经网络进行同步分析

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

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-06-11 DOI:10.1002/acs.3856

Zhenjiang Liu, Yi-Fei Pu

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引用次数: 0

摘要

本文研究了一类具有时变延迟的模糊惯性神经网络（FINN）的渐近同步问题。首先，采用直接分析方法取代惯用的变量变换和降阶法来处理惯性项。其次，设计了合适的 Lyapunov 函数和控制方案，从而获得了几个新的充分条件，以保证具有时变延迟的 FINN 的渐近同步。结果表明，所获得的条件更简单、更有效。同时，一些数值示例证明了所提策略的有效性，并验证了理论结果。

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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Synchronization analysis of fuzzy inertial neural networks with time-varying delays via non-reduced order method

In this article, the asymptotic synchronization of a class of fuzzy inertial neural networks (FINNs) with time-varying delays is investigated. First, the direct analysis approach is applied to replace the accustomed variable transformation and the reduced-order method for addressing the inertial term. Second, a suitable Lyapunov function and control scheme are devised to obtain several new sufficient conditions to guarantee the asymptotic synchronization of the class of FINNs with time-varying delays. It turns out that the obtained criteria are simpler and more effective. Meanwhile, some numerical examples demonstrate the effectiveness of the proposed strategies and verify the theoretical results.

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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.