Output sampling synchronization and state estimation in flux-charge domain memristive neural networks with leakage and time-varying delays

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
G. Soundararajan , R. Suvetha , Minvydas Ragulskis , P. Prakash
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引用次数: 0

Abstract

This paper theoretically explores the coexistence of synchronization and state estimation analysis through output sampling measures for a class of memristive neural networks operating within the flux-charge domain. These networks are subject to constant delayed responses in self-feedback loops and time-varying delayed responses incorporated into the activation functions. A contemporary output sampling controller is designed to discretize system dynamics based on available output measurements, which enhances control performance by minimizing update frequency, thus overcoming network bandwidth limitations and addressing network synchronization and state vector estimation. By utilizing differential inclusion mapping to capture weights from discontinuous memristive switching actions and an input-delay approach to bound nonuniform sampling intervals, we present linear matrix inequality-based sufficient conditions for synchronization and vector estimation criteria under the Lyapunov–Krasovskii functional framework and relaxed integral inequality. Finally, by utilizing the preset experimental data-set, we visually verify the adaptability of the proposed theoretical findings concerning synchronization, anti-synchronization, and vector state estimation of delayed memristive neural networks operating in the flux-charge domain. Furthermore, numerical validation through simulation demonstrates the impact of leakage delay and output measurement sampling by comparative analysis with scenarios lacking leakage and sampling measurements.
具有泄漏和时变延迟的磁荷域记忆神经网络的输出采样同步与状态估计。
本文从理论上探讨了在磁荷域内运行的一类记忆神经网络的同步和状态估计分析共存的输出采样措施。这些网络在自反馈回路中有恒定的延迟响应,在激活函数中有时变的延迟响应。现代输出采样控制器设计基于可用的输出测量离散系统动力学,通过最小化更新频率来提高控制性能,从而克服网络带宽限制,解决网络同步和状态向量估计问题。利用微分包含映射捕获不连续记忆开关动作的权值,利用输入延迟方法求解有界非均匀采样区间,给出了基于线性矩阵不等式的同步充分条件和Lyapunov-Krasovskii泛函框架下的矢量估计准则和松弛积分不等式。最后,利用预先设定的实验数据集,我们直观地验证了在磁荷域运行的延迟记忆神经网络的同步、反同步和矢量状态估计的理论发现的适应性。此外,通过与无泄漏和采样测量场景的对比分析,通过仿真验证了泄漏延迟和输出测量采样的影响。
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
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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