@article{Sun2022, 
author = {Baoyan Sun and Jun Hu and Yan Gao},
title = {Variance-constrained robust        H          ∞       state estimation for discrete time-varying uncertain neural networks with uniform quantization},
year = {2022},
journal = {AIMS Mathematics},
volume = {7},
number = {8},
pages = {14227-14248},
keywords = {discrete time-varying uncertain neural networks, uniform quantization, variance constraint, H∞ performance requirement},
url = {https://www.sciopen.com/article/10.3934/math.2022784},
doi = {10.3934/math.2022784},
abstract = {In this paper, we consider the robust        H          ∞       state estimation (SE) problem for a class of discrete time-varying uncertain neural networks (DTVUNNs) with uniform quantization and time-delay under variance constraints. In order to reflect the actual situation for the dynamic system, the constant time-delay is considered. In addition, the measurement output is first quantized by a uniform quantizer and then transmitted through a communication channel. The main purpose is to design a time-varying finite-horizon state estimator such that, for both the uniform quantization and time-delay, some sufficient criteria are obtained for the estimation error (EE) system to satisfy the error variance boundedness and the        H          ∞       performance constraint. With the help of stochastic analysis technique, a new        H          ∞       SE algorithm without resorting the augmentation method is proposed for DTVUNNs with uniform quantization. Finally, a simulation example is given to illustrate the feasibility and validity of the proposed variance-constrained robust        H          ∞       SE method.}
}