受传感器饱和影响的基于 Memristor 的具有混合时间延迟的分数阶神经网络的量化非脆弱状态估计

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Xiaoguang Shao, Yanjuan Lu, Jie Zhang, Ming Lyu, Yu Yang
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引用次数: 0

摘要

本研究探讨了具有混合随机延迟的基于忆阻器的分数阶神经网络的非脆弱状态估计问题。考虑到信号传输信道的带宽有限,研究引入了定量处理,以减轻网络负担,防止信号阻塞和数据包丢失。在实际环境中,所设计的估计器可能会出现潜在的增益变化。为解决这一问题,我们开发了一种分数阶非脆弱估计器,将对数量化器纳入其中,最终提高了状态估计器的可靠性。此外,通过将广义分数阶 Lyapunov 直接法与新颖的 Caputo-Wirtinger 积分不等式相结合,得出了一个较低的保守准则,以保证增强系统的渐进稳定性。最后,通过两个仿真实例证明了所需估计方案的准确性和实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantized Nonfragile State Estimation of Memristor-Based Fractional-Order Neural Networks with Hybrid Time Delays Subject to Sensor Saturations
This study addresses the issue of nonfragile state estimation for memristor-based fractional-order neural networks with hybrid randomly occurring delays. Considering the finite bandwidth of the signal transmission channel, quantitative processing is introduced to reduce network burden and prevent signal blocking and packet loss. In a real-world setting, the designed estimator may experience potential gain variations. To address this issue, a fractional-order nonfragile estimator is developed by incorporating a logarithmic quantizer, which ultimately improves the reliability of the state estimator. In addition, by combining the generalized fractional-order Lyapunov direct method with novel Caputo–Wirtinger integral inequalities, a lower conservative criterion is derived to guarantee the asymptotic stability of the augmented system. At last, the accuracy and practicality of the desired estimation scheme are demonstrated through two simulation examples.
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
期刊介绍: ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric. Indexed/​Abstracted: Web of Science SCIE Scopus CAS INSPEC Portico
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