Active federated transfer algorithm based on broad learning for fault diagnosis
Published 2023 View Full Article
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Title
Active federated transfer algorithm based on broad learning for fault diagnosis
Authors
Keywords
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Journal
MEASUREMENT
Volume 208, Issue -, Pages 112452
Publisher
Elsevier BV
Online
2023-01-10
DOI
10.1016/j.measurement.2023.112452
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- (2020) Jie Du et al. IEEE Transactions on Cybernetics
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- (2019) Qiang Yang et al. ACM Transactions on Intelligent Systems and Technology
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- Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture
- (2018) C. L. Philip Chen et al. IEEE Transactions on Neural Networks and Learning Systems
- Universal Approximation Capability of Broad Learning System and Its Structural Variations
- (2018) C. L. Philip Chen et al. IEEE Transactions on Neural Networks and Learning Systems
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- (2017) Andrew Kusiak NATURE
- Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study
- (2015) Wade A. Smith et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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