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

Palm, G; Poggio, T

doi:10.1137/0134041

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

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Palm, G
Former Department Structure and Function of Natural Nerve-Net , Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Poggio, T
Former Department Information Processing in Insects, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://epubs.siam.org/doi/abs/10.1137/0134041
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Citation

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.

Cite as: https://hdl.handle.net/21.11116/0000-0006-820B-B

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.