The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Improved Computation for Levenberg–Marquardt Training
Abstract
The improved computation presented in this paper is aimed to optimize the neural networks learning process using Levenberg-Marquardt (LM) algorithm. Quasi-Hessian matrix and gradient vector are computed directly, without Jacobian matrix multiplication and storage. The memory limitation problem for LM training is solved. Considering the symmetry of quasi-Hessian matrix, only elements in its upper/lower triangular array need to be calculated. Therefore, training speed is improved significantly, not only because of the smaller array stored in memory, but also the reduced operations in quasi-Hessian matrix calculation. The improved memory and time efficiencies are especially true for large sized patterns training.
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Cites
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A method for the solution of certain non-linear problems in least squares
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Self-citationFault Diagnostic System for a Multilevel Inverter Using a Neural Network
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Backpropagation: past and future
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Real-time non-rigid reconstruction using an RGB-D camera
Zollhöfer, M., Nießner, M., Izadi, S., Rehmann, C., Zach, C., Fisher, M., Wu, C., Fitzgibbon, A., Loop, C., Theobalt, C., et al. (2014) · ACM Transactions on Graphics · doi:10.1145/2601097.2601165 · Cited by 405
A Survey on SCADA Systems: Secure Protocols, Incidents, Threats and Tactics
Pliatsios, D., Sarigiannidis, P., Lagkas, T., & Sarigiannidis, A. G. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2987688 · Cited by 320
“Linda et al. [85] presented an anomaly-based intrusion detection system based on the combination of two neural network algorithms, namely the Levenberg-Marquardt [86] and the Error Back-Propagation [87] algorithms.”
MethodEnhancing transportation systems via deep learning: A survey
Wang, Y., Zhang, D., Liu, Y., Dai, B., & Lee, L. H. (2019) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.12.004 · Cited by 312
Deshpande, G., Wang, P., Rangaprakash, D., & Wilamowski, B. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2379621 · Cited by 161
Self-citationAnalyses of Triaxial Accelerometer Calibration Algorithms
Sipos, M., Paces, P., Rohac, J., & Novacek, P. (2012) · IEEE Sensors Journal · doi:10.1109/jsen.2011.2167319 · Cited by 145
Metadata
- Authors
- Wilamowski, B. M., & Yu, H.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2010-06
- Volume
- 21
- Issue
- 6
- Pages
- 930-937
- ISSN
- 1045-9227 (ISSN-L)
- 1941-0093
- OpenAlex
- W2133764509
- Cited by
- 622
- References
- 17
- FWCI
- 26.53
- Type
- journal article
- Language
- en
- Open access
- closed
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