A Computation Modification for Multi-layered Neural Network Using Extended Kalman Filter
Pages 114 - 117
Abstract
A lot of learning algorithms for deep layered network are sincerely suffered from complex computation and slow convergence because of a very large number of free parameters. We need to develop an efficient algorithm for deep neural network. The Kalman filter concept can be applied to parameter estimation of neural network to improve computation performance. The algorithms based extended Kalman filter has a serious drawback in its computational complexity. We discuss how a fast algorithm should be developed for reduction in computation time.
References
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Index Terms
- A Computation Modification for Multi-layered Neural Network Using Extended Kalman Filter
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Published In
January 2018
310 pages
ISBN:9781450363396
DOI:10.1145/3177457
Copyright © 2018 ACM.
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In-Cooperation
- University of Canberra: University of Canberra
- University of Technology Sydney
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 08 January 2018
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- Refereed limited
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ICCMS 2018
ICCMS 2018: The 10th International Conference on Computer Modeling and Simulation
January 8 - 10, 2018
Sydney, Australia
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