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Fading memory and the problem of approximating nonlinear operators with Volterra series
Abstract
Using the notion of fading memory we prove very strong versions of two folk theorems. The first is that any time-invariant (TI) continuous nonlinear operator can be approximated by a Volterra series operator, and the second is that the approximating operator can be realized as a finite-dimensional linear dynamical system with a nonlinear readout map. While previous approximation results are valid over finite time intervals and for signals in compact sets, the approximations presented here hold for all time and for signals in useful (noncompact) sets. The discretetime analog of the second theorem asserts that any TI operator with fading memory can be approximated (in our strong sense) by a nonlinear moving- average operator. Some further discussion of the notion of fading memory is given.
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Tanaka, G., Yamane, T., Héroux, J. B., Nakane, R., Kanazawa, N., Takeda, S., Numata, H., Nakano, D., & Hirose, A. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.03.005 · Cited by 2,146
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Bertschinger, N., & Natschläger, T. (2004) · Neural Computation · doi:10.1162/089976604323057443 · Cited by 833
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Metadata
- Authors
- Boyd, S., & Chua, L.
- Venue
- IEEE Transactions on Circuits and Systems
- Published
- 1985-11
- Volume
- 32
- Issue
- 11
- Pages
- 1150-1161
- ISSN
- 0098-4094 (ISSN-L)
- OpenAlex
- W2160068178
- Cited by
- 991
- References
- 10
- FWCI
- 4.86
- Type
- journal article
- Language
- en
- Open access
- closed
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