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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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