The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
An iterative method for the identification of nonlinear systems using a Hammerstein model
Abstract
An iterative method is proposed for the identification of nonlinear systems from samples of inputs and outputs in the presence of noise. The model used for the identification consists of a no-memory gain (of an assumed polynomial form) followed by a linear discrete system. The parameters of the pulse transfer function of the linear system and the coefficients of the polynomial non-linearity are alternately adjusted to minimize a mean square error criterion. Digital computer simulations are included to demonstrate the feasibility of the technique.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A technique for the identification of linear systems
Steiglitz, K., & McBride, L. (1965) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1965.1098181 · Cited by 641
Zadeh, L. (1956) · IRE Transactions on Circuit Theory · doi:10.1109/tct.1956.1086328 · Cited by 156
Cited by
Identification of Hammerstein–Wiener models
Wills, A., Schön, T. B., Ljung, L., & Ninness, B. (2013) · Automatica · doi:10.1016/j.automatica.2012.09.018 · Cited by 295
Inferring input nonlinearities in neural encoding models
Ahrens, M. B., Paninski, L., & Sahani, M. (2008) · Network Computation in Neural Systems · doi:10.1080/09548980701813936 · Cited by 72
Metadata
- Authors
- Narendra, K., & Gallman, P.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 1966-07
- Volume
- 11
- Issue
- 3
- Pages
- 546-550
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2072627349
- Cited by
- 699
- References
- 2
- FWCI
- 2.35
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An iterative method for the identification of nonlinear systems using a Hammerstein model. https://app.cochat.ai/papers/10.1109/tac.1966.1098387
- The paper
- Narendra, K., & Gallman, P. (1966). An iterative method for the identification of nonlinear systems using a Hammerstein model. IEEE Transactions on Automatic Control. https://doi.org/10.1109/tac.1966.1098387
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.
