Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31531
Title: Proportional-Integral-Observer-Based Fusion Estimation for Artificial Neural Networks: Implementing a One-Bit Encoding Scheme
Authors: Zhu, K
Wang, Z
Ding, D
Hu, J
Dong, H
Keywords: artificial neural networks;set-membership state estimation;fusion estimation;proportional-integral-observer;one-bit encoding mechanism.
Issue Date: 9-May-2025
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Zhu, K. et al. (2025) 'Proportional-Integral-Observer-Based Fusion Estimation for Artificial Neural Networks: Implementing a One-Bit Encoding Scheme', IEEE Transactions on Neural Networks and Learning Systems, 0 (early access), pp. 1 - 11. doi: 10.1109/TNNLS.2025.3556370.
Abstract: This article is concerned with the proportional-integral-observer (PIO)-based fusion estimation problem for a class of artificial neural networks (ANNs) equipped with multiple sensors, which are constrained by bandwidth and subjected to unknown-but-bounded noises (UBBNs). For the purpose of efficient information communication, an approach known as the one-bit encoding mechanism (OBEM) is proposed that enables the encoding of scalar data using merely a single bit. Then, a local PIO-based set-membership estimator is devised for each sensor node, with the aim of achieving the desired estimation task while considering the possible data distortion due to OBEM and the existence of UBBNs. Subsequently, sufficient conditions are established to ensure the existence and effectiveness of the PIO-based set-membership estimator. Moreover, to enhance the global estimation performance, an ellipsoid-based fusion rule is introduced for all local PIO-based set-membership estimators. The performance of fusion estimation is then analyzed using set theory and the optimization method, leading to the determination of relevant parameters. Finally, the effectiveness and advantages of the proposed estimation algorithm are demonstrated through a simulation example.
URI: https://bura.brunel.ac.uk/handle/2438/31531
DOI: https://doi.org/10.1109/TNNLS.2025.3556370
ISSN: 2162-237X
Other Identifiers: ORCiD: Kaiqun Zhu https://orcid.org/0000-0002-0658-0806
ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401
ORCiD: Derui Ding https://orcid.org/0000-0001-7402-6682
ORCiD: Jun Hu https://orcid.org/0000-0002-7852-5064
ORCiD: Hongli Dong https://orcid.org/0000-0001-8531-6757
Appears in Collections:Dept of Computer Science Research Papers

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