The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Nonlinear black-box modeling in system identification: a unified overview
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Cites
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
Daubechies, I. (1992) · doi:10.1137/1.9781611970104 · Cited by 15,979
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
The wavelet transform, time-frequency localization and signal analysis
Daubechies, I. (1990) · IEEE Transactions on Information Theory · doi:10.1109/18.57199 · Cited by 6,483
Nadaraya, E. A. (1964) · Theory of Probability & Its Applications · doi:10.1137/1109020 · Cited by 3,462
Wold, S., Ruhe, A., Wold, H., & Dunn, III (1984) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0905052 · Cited by 2,529
Huber, P. J. (1985) · The Annals of Statistics · doi:10.1214/aos/1176349519 · Cited by 1,277
Ill-Conditioning in Neural Network Training Problems
Saarinen, S., Bramley, R., & Cybenko, G. (1993) · SIAM Journal on Scientific Computing · doi:10.1137/0914044 · Cited by 141
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Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review
Zendehboudi, S., Rezaei, N., & Lohi, A. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.06.051 · Cited by 394
Identification of piecewise affine systems via mixed-integer programming
Roll, J., Bemporad, A., & Ljung, L. (2004) · Automatica · doi:10.1016/j.automatica.2003.08.006 · Cited by 386
Self-citationDeep learning in robotics: A review of recent research
Pierson, H. A., & Gashler, M. S. (2017) · Advanced Robotics · doi:10.1080/01691864.2017.1365009 · Cited by 331
Ciallella, H. L., & Zhu, H. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.8b00393 · Cited by 227
A Differentiable Physics Engine for Deep Learning in Robotics
Degrave, J., Hermans, M., Dambre, J., & wyffels, F. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00006 · Cited by 182
Metadata
- Authors
- Sjöberg, J., Zhang, Q., Ljung, L., Benveniste, A., Delyon, B., Glorennec, P.-Y., Hjalmarsson, H., & Juditsky, A.
- Venue
- Automatica
- Publisher
- Elsevier BV
- Published
- 1995-12
- Volume
- 31
- Issue
- 12
- Pages
- 1691-1724
- ISSN
- 0005-1098 (ISSN-L)
- OpenAlex
- W2040135606
- Cited by
- 1,942
- References
- 8
- FWCI
- 77.06
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Nonlinear black-box modeling in system identification: a unified overview. https://app.cochat.ai/papers/10.1016/0005-1098(95)00120-8
- The paper
- Sjöberg, J., Zhang, Q., Ljung, L., Benveniste, A., Delyon, B., Glorennec, P.-Y., Hjalmarsson, H., & Juditsky, A. (1995). Nonlinear black-box modeling in system identification: a unified overview. Automatica. https://doi.org/10.1016/0005-1098(95)00120-8
Data sources
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.
