State estimation for semi-Markovian switching CVNNs with quantization effects and linear fractional uncertainties

被引:4
|
作者
Li, Qiang [1 ]
Liang, Jinling [1 ]
Gong, Weiqiang [2 ]
机构
[1] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China
[2] Nanjing Univ Finance & Econ, Sch Appl Math, Nanjing 210023, Peoples R China
基金
中国国家自然科学基金; 美国国家科学基金会;
关键词
RECURRENT NEURAL-NETWORKS; STABILITY ANALYSIS; DISCRETE-TIME; UNIFORM QUANTIZATIONS; STOCHASTIC STABILITY; JUMP SYSTEMS; SYNCHRONIZATION; STABILIZATION; DELAYS;
D O I
10.1016/j.jfranklin.2021.05.035
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper is concerned with the robust state estimation problem for semi-Markovian switching complex-valued neural networks with quantization effects (QEs). The uncertain parameters are described by the linear fractional uncertainties (LFUs). To enhance the channel utilization and save the communication resources, the measured output is quantized before transmission by a logarithmic quantizer. The purpose of the problem under consideration is to design a full-order state estimator to estimate the complex-valued neuron states. Based on the Lyapunov stability theory, stochastic analysis method, and some improved integral inequalities, sufficient conditions are first derived to guarantee the estimation error system to be globally asymptotically stable in the mean square. Then, the desired state estimator can be directly designed after solving a set of matrix inequalities, which is robust against the LFUs and the QEs. In the end of the paper, one numerical example is provided to illustrate the feasibility and effectiveness of the proposed estimation design scheme. (C) 2021 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:6326 / 6347
页数:22
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