Paper Info
Title
New result on reliable H∞ performance state estimation for memory static neural networks with stochastic sampled-data communication.
Abstract
This work examines the H∞ performance state estimation problem for memory static neural networks (MSNNs) with reliable state feedback stochastic sampled-data control (SSDC). The purpose of presenting this study is to determine whether the H∞ performance and criteria with less conservatism for stability could be gained by SSDC for MSNNs or not. Firstly, we suppose that the sampling interval values follow Bernoulli distribution and the probability of occurrence are teadfast constant, then generalize it to a more universal form. Secondly, on basis of considering the sampling input delay and its sawtooth structure characteristics, a modified augmented Lyapunov-Krasovskii functional (LKF) is constructed on account of the free-matrix-based integral inequality (FMBII) together with generalized free-weighting-matrix (GFWM) inequality, which can reduce the conservatism of H∞ performance criteria. Thirdly, the expected estimator gain matrix can be designed in the light of the solution to linear matrix inequalities (LMIs). Finally, an numerical example is given to check the superiority of the proposed MSNNs control design technique.
Year
DOI
Venue
2020
10.1016/j.amc.2019.124619
Applied Mathematics and Computation
Keywords
Field
DocType
Memory static neural networks,H∞ state estimation,Stochastic sampling,Reliable control
Bernoulli distribution,Applied mathematics,Mathematical optimization,Sampling interval,Matrix (mathematics),Conservatism,Sampling (statistics),Artificial neural network,Sawtooth wave,Mathematics,Estimator
Journal
Volume
ISSN
Citations 
364
0096-3003
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Shiyu Dong1102.17
Hong Zhu21327.44
Shouming Zhong31470121.41
Kaibo Shi421325.47
Jun Cheng553643.22
Wei Kang6424.61