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  Stochastic Identification Methods for Nonlinear Systems: an Extension of the Wiener Theory

Palm, G., & Poggio, T. (1978). Stochastic Identification Methods for Nonlinear Systems: an Extension of the Wiener Theory. SIAM Journal on Applied Mathematics, 34(3), 524-534. doi:10.1137/0134041.

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https://epubs.siam.org/doi/abs/10.1137/0134041 (Publisher version)
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 Creators:
Palm, G1, 2, Author              
Poggio, T2, 3, Author              
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1Former Department Structure and Function of Natural Nerve-Net , Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497803              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Former Department Information Processing in Insects, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497801              

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 Abstract: The Wiener series provides a representation for many nonlinear systems with respect to Brownian motion inputs. In this paper the Wiener theory is extended to a wide class of stochastic inputs including Brownian motion (and white-noise). In particular, difficulties intrinsic to cross-correlation methods (like the Lee–Schetzen method) are discussed for several discrete-time random input processes used in biological applications.

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 Dates: 1976-091978-07
 Publication Status: Published in print
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 Identifiers: DOI: 10.1137/0134041
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Title: SIAM Journal on Applied Mathematics
Source Genre: Journal
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Publ. Info: Philadelphia, PA : Society for Industrial and Applied Mathematics
Pages: - Volume / Issue: 34 (3) Sequence Number: - Start / End Page: 524 - 534 Identifier: ISSN: 0036-1399
CoNE: https://pure.mpg.de/cone/journals/resource/110975500577317