Paper Info
Title
A Recursive Approach to Quantized <inline-formula> <tex-math notation="LaTeX">${H_{\infty}}$ </tex-math></inline-formula> State Estimation for Genetic Regulatory Networks Under Stochastic Communication Protocols
Abstract
This paper deals with the finite-horizon quantized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> state estimation problem for a class of discrete time-varying genetic regulatory networks with quantization effects under stochastic communication protocols (SCPs). To better reflect the data-driven flavor of today’s biological research, the network measurements (typically gigabytes in size by high-throughput sequencing technologies) are transmitted to a remote state estimator via two independent communication networks of limited bandwidths. To lighten the communication loads and avoid undesired data collisions, the measurement outputs are quantized and then transmitted under two SCPs introduced to schedule the large-scale data transmissions. The purpose of this paper is to design a time-varying state estimator such that the error dynamics of the state estimation satisfies a prescribed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> performance requirement over a finite horizon in the presence of nonlinearities, quantization effects, and SCPs. By utilizing the completing-the-square technique, sufficient conditions are derived to ensure the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> estimation performance and the parameters of the state estimator are designed by solving coupled backward recursive Riccati difference equations. A numerical example is given to illustrate the effectiveness of the design scheme of the proposed state estimator.
Year
DOI
Venue
2019
10.1109/TNNLS.2018.2885723
IEEE Transactions on Neural Networks and Learning Systems
Keywords
Field
DocType
State estimation,Protocols,Quantization (signal),Communication networks,Proteins,Schedules
Performance requirement,Telecommunications network,State estimator,Computer science,Algorithm,Schedule,Artificial intelligence,Quantization (physics),Quantization (signal processing),Machine learning,Recursion,Communications protocol
Journal
Volume
Issue
ISSN
30
9
2162-237X
Citations 
PageRank 
References 
8
0.44
17
Authors
4
Name
Order
Citations
PageRank
Xiongbo Wan1595.68
Zidong Wang211003578.11
Qing-Long Han36396315.39
Min Wu43582272.55