The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Kalman Filtering and Neural Networks
Abstract
From the Publisher: Kalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks. This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear. The book deals with important applications in such fields as control, financial forecasting, and idle speed control.
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Citations
Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
The pricing of options and corporate liabilities
Black, F., & Scholes, M. (1973) · Journal of Political Economy · doi:10.1086/260062 · Cited by 29,074
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993) · IEE Proceedings F Radar and Signal Processing · doi:10.1049/ip-f-2.1993.0015 · Cited by 7,550
Sequential Monte Carlo Methods in Practice
(2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,366
Stochastic Processes and Filtering Theory
(1970) · Mathematics in Science and Engineering · doi:10.1016/s0076-5392(09)x6022-4 · Cited by 7,302
Introduction to the theory of neural computation
(1994) · Neural Networks · doi:10.1016/0893-6080(94)90014-0 · Cited by 6,401
New extension of the Kalman filter to nonlinear systems
Julier, S. J., & Uhlmann, J. K. (1997) · SPIE Proceedings · doi:10.1117/12.280797 · Cited by 5,331
Oscillation and Chaos in Physiological Control Systems
Mackey, M. C., & Glass, L. (1977) · Science · doi:10.1126/science.267326 · Cited by 4,135
The unscented Kalman filter for nonlinear estimation
Wan, E. A., & Van Der Merwe, R. (2002) · Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373) · doi:10.1109/asspcc.2000.882463 · Cited by 4,001
Packard, N. H., Crutchfield, J. P., Farmer, J. D., & Shaw, R. S. (1980) · Physical Review Letters · doi:10.1103/physrevlett.45.712 · Cited by 3,869
Cited by
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768
The emulation theory of representation: Motor control, imagery, and perception
Grush, R. (2004) · Behavioral and Brain Sciences · doi:10.1017/s0140525x04000093 · Cited by 1,387
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.031 · Cited by 1,385
Waag, W., Fleischer, C., & Sauer, D. U. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.02.064 · Cited by 1,059
Metadata
- Venue
- Wiley eBooks
- Publisher
- Wiley
- Published
- 2001-10
- OpenAlex
- W1581808154
- Cited by
- 2,386
- References
- 39
- FWCI
- 3.88
- Type
- book
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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