The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
High-order neural network structures for identification of dynamical systems
Abstract
Several continuous-time and discrete-time recurrent neural network models have been developed and applied to various engineering problems. One of the difficulties encountered in the application of recurrent networks is the derivation of efficient learning algorithms that also guarantee the stability of the overall system. This paper studies the approximation and learning properties of one class of recurrent networks, known as high-order neural networks; and applies these architectures to the identification of dynamical systems. In recurrent high-order neural networks, the dynamic components are distributed throughout the network in the form of dynamic neurons. It is shown that if enough high-order connections are allowed then this network is capable of approximating arbitrary dynamical systems. Identification schemes based on high-order network architectures are designed and analyzed.
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Citations
Cites
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Hopfield, J. J. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.10.3088 · Cited by 6,918
Cited by
Adaptive neural network predictive control for nonlinear pure feedback systems with input delay
Na, J., Ren, X., Shang, C., & Guo, Y. (2012) · Journal of Process Control · doi:10.1016/j.jprocont.2011.09.003 · Cited by 101
Metadata
- Authors
- Kosmatopoulos, E. B., Polycarpou, M. M., Christodoulou, M. A., & Ioannou, P. A.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1995-03
- Volume
- 6
- Issue
- 2
- Pages
- 422-431
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2146014298
- Cited by
- 802
- References
- 2
- FWCI
- 15.32
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: High-order neural network structures for identification of dynamical systems. https://app.cochat.ai/papers/10.1109/72.363477
- The paper
- Kosmatopoulos, E. B., Polycarpou, M. M., Christodoulou, M. A., & Ioannou, P. A. (1995). High-order neural network structures for identification of dynamical systems. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.363477
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