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Neural Moving Horizon Estimation: A Systematic Literature Review

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Gepubliceerd in:Electronics vol. 14, no. 10 (2025), p. 1954
Hoofdauteur: Surrayya, Mobeen
Andere auteurs: Jann, Cristobal, Singoji Shashank, Basaam, Rassas, Izadi Mohammadreza, Shayan Zeinab, Amin, Yazdanshenas, Sohi, Harneet Kaur, Barnsley, Robert, Elliott, Lana, Faieghi Reza
Gepubliceerd in:
MDPI AG
Onderwerpen:
Machine learning
Accuracy
Radial basis function
Learning
Mathematical models
Multilayers
Cost function
Estimates
Multilayer perceptrons
Optimization
Process controls
State estimation
Design
Networks
Kalman filters
Real time
Systematic review
Parameter estimation
Literature reviews
Approximation
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Samenvatting:The neural moving horizon estimator (NMHE) is a relatively new and powerful state estimator that combines the strengths of neural networks (NNs) and model-based state estimation techniques. Various approaches exist for constructing NMHEs, each with its unique advantages and limitations. However, a comprehensive literature review that consolidates existing knowledge, outlines design guidelines, and highlights future research directions is currently lacking. To address this gap, this systematic review screened 1164 records and ultimately included 22 primary studies, following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. This paper (1) explains the fundamental principles of NMHEs, (2) explores three major NMHE architectures, (3) analyzes the types of NNs used, such as multi-layer perceptrons (MLPs), long short-term memory networks (LSTMs), radial basis function networks (RBFs), and fuzzy neural networks, (4) reviews real-time implementability—including reported execution times ranging from 1.6 μs to 11.28 s on different computing hardware—and (5) identifies common limitations and future research directions. The findings show that NMHEs can be realized in three principal ways: model learning, cost function learning, and approximating the real-time optimization in moving horizon estimation. Cost function learning offers flexibility in capturing task-specific estimation goals, while model learning and optimization approximation approaches tend to improve estimation accuracy and computational speed, respectively.
ISSN:2079-9292
DOI:10.3390/electronics14101954
Bron:Advanced Technologies & Aerospace Database