Finite-Horizon Variance-Constrained H∞ Estimation for Complex Networks Subject to Dynamical Bias Using Binary Encoding Schemes

In this article, a variance-constrained H ∞ state estimation issue is dealt with for a type of nonlinear time-varying complex networks affected by dynamical bias under binary encoding schemes (BESs). The BESs are used during signal transmission in view of the security of binary bit strings. The stoc...

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Vydáno v:IEEE access s. 1
Hlavní autoři: Li, Weijian, Hou, Nan, Yang, Fan, Bu, Xianye, Sun, Ligang
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 11.12.2023
Témata:
binary encoding schemes
Complex networks
dynamical bias
Encoding
Estimation
Q measurement
Quantization (signal)
stochastic nonlinearities
Stochastic processes
Time-varying complex networks
Upper bound
variance-constrained state estimation
ISSN:2169-3536
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Popis
Shrnutí:In this article, a variance-constrained H ∞ state estimation issue is dealt with for a type of nonlinear time-varying complex networks affected by dynamical bias under binary encoding schemes (BESs). The BESs are used during signal transmission in view of the security of binary bit strings. The stochastic bias is involved using a dynamical equation, and stochastic nonlinearity is characterized by statistical property. The purpose of this article is to construct a finite-horizon state estimator, such that the estimation error dynamics satisfies performance requirements of both the prescribed upper bound constraint on the error variance and the H ∞ noise rejection. By employing the matrix inequality approach and random analysis, sufficient conditions are established for the presence of the state estimator. Subsequently, the gain parameters of the constructed estimator are acquired by solving some recursive matrix inequalities. Ultimately, the correctness of the developed estimation algorithm is testified via a numerical simulation example.
ISSN:2169-3536
DOI:10.1109/ACCESS.2023.3341425