Future Different-Layer Linear Equation and Bounded Inequality Solved by Combining Adams–Bashforth Methods With CZNN Model

Qiu, Binbin; Zhang, Yunong

doi:10.1109/tie.2020.2970669

Cited by 15 publications

(1 citation statement)

References 34 publications

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“…Classical time-varying problems are single-layered, which have relatively weak ability to describe practical problems [36][37][38][39][40]. Recently, by introducing multilayers in time-varying problems, multilayered time-varying problems are investigated [41][42][43][44][45]. For example, in [37], multilayered time-varying linear systems including equality and inequality layers were investigated by ZND method.…”

Section: Introductionmentioning

confidence: 99%

Noise-Tolerant Zeroing Neural Dynamics for Solving Hybrid Multilayered Time-Varying Linear Equation System

Shi

Sun

et al. 2022

Security and Communication Networks

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Resource allocation problem in a wireless sensor network can be formulated as time-varying optimization, which can be further converted as time-varying linear equation system (TVLES). Hybrid multilayered time-varying linear equation system (HMTVLES) involving hybrid multilayers and time-variation characteristic is a complicated and challenging problem. Recently it has been solved by zeroing neural dynamics (ZND) method under ideal conditions, i.e., without noises. However, noises are ubiquitous, immanent and unavoidable in real-time systems. In this work, we propose a noise-tolerant zeroing neural dynamics (NTZND) model for solving HMTVLES. It can deal with different kinds of noises such as constant noise, linear-increasing noise, and random noise. Theoretical analyses guarantee the precision of NTZND model in the presence of different kinds of noises. In addition, a general NTZND model is proposed based on general activation function. Besides, classical ZND method and gradient neural dynamics (GND) method are also investigated and compared. Numerical experimental results are presented to verify the theoretical results of proposed models.

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