The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems
Abstract
The paper investigates error-entropy-minimization in adaptive systems training. We prove the equivalence between minimization of error's Renyi (1970) entropy of order /spl alpha/ and minimization of a Csiszar (1981) distance measure between the densities of desired and system outputs. A nonparametric estimator for Renyi's entropy is presented, and it is shown that the global minimum of this estimator is the same as the actual entropy. The performance of the error-entropy-minimization criterion is compared with mean-square-error-minimization in the short-term prediction of a chaotic time series and in nonlinear system identification.
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Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Probability, Random Variables, and Stochastic Processes.
Abrahams, J., & Papoulis, A. (1984) · Journal of the American Statistical Association · doi:10.2307/2288754 · Cited by 16,226
On Estimation of a Probability Density Function and Mode
Parzen, E. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704472 · Cited by 10,585
Neural networks: A comprehensive foundation
Tadeusiewicz, R. (1995) · Control Engineering Practice · doi:10.1016/0967-0661(95)90080-2 · Cited by 7,218
Information theory and statistics
(1959) · Journal of the Franklin Institute · doi:10.1016/0016-0032(59)90368-0 · Cited by 7,094
Extrapolation, Interpolation, and Smoothing of Stationary Time Series: With Engineering Applications
Wiener, N. (1949) · The MIT Press eBooks · doi:10.7551/mitpress/2946.001.0001 · Cited by 4,826
Linear and nonlinear programming.
(1986) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(86)90092-3 · Cited by 4,805
Information Theory: Coding Theorems for Discrete Memoryless Systems
Csiszár, I., & Körner, J. (2014) · REAL-EOD (Library of the Hungarian Academy of Sciences and the Information Center Oriental Collection) · Cited by 3,246
Metadata
- Authors
- Erdogmus, D., & Principe, J. C.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2002-07
- Volume
- 50
- Issue
- 7
- Pages
- 1780-1786
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2097521973
- Cited by
- 353
- References
- 25
- FWCI
- 12.00
- Type
- journal article
- Language
- en
- Open access
- closed
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- Erdogmus, D., & Principe, J. C. (2002). An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2002.1011217
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
