The identification of nonlinear biological systems: Volterra kernel approaches
- PMID: 8841729
- DOI: 10.1007/BF02648117
The identification of nonlinear biological systems: Volterra kernel approaches
Abstract
Representation, identification, and modeling are investigated for nonlinear biomedical systems. We begin by considering the conditions under which a nonlinear system can be represented or accurately approximated by a Volterra series (or functional expansion). Next, we examine system identification through estimating the kernels in a Volterra functional expansion approximation for the system. A recent kernel estimation technique that has proved to be effective in a number of biomedical applications is investigated as to running time and demonstrated on both clean and noisy data records, then it is used to illustrate identification of cascades of alternating dynamic linear and static nonlinear systems, both single-input single-output and multivariable cascades. During the presentation, we critically examine some interesting biological applications of kernel estimation techniques.
Similar articles
-
The identification of nonlinear biological systems: Volterra kernel approaches.Ann Biomed Eng. 1996 Mar-Apr;24(2):250-68. doi: 10.1007/BF02667354. Ann Biomed Eng. 1996. PMID: 8678357
-
Use of meixner functions in estimation of Volterra kernels of nonlinear systems with delay.IEEE Trans Biomed Eng. 2005 Feb;52(2):229-37. doi: 10.1109/TBME.2004.840187. IEEE Trans Biomed Eng. 2005. PMID: 15709660
-
Parallel cascade identification and kernel estimation for nonlinear systems.Ann Biomed Eng. 1991;19(4):429-55. doi: 10.1007/BF02584319. Ann Biomed Eng. 1991. PMID: 1741525
-
The identification of nonlinear biological systems: Wiener kernel approaches.Ann Biomed Eng. 1990;18(6):629-54. doi: 10.1007/BF02368452. Ann Biomed Eng. 1990. PMID: 2281885 Review.
-
Nonlinear System Identification of Neural Systems from Neurophysiological Signals.Neuroscience. 2021 Mar 15;458:213-228. doi: 10.1016/j.neuroscience.2020.12.001. Epub 2020 Dec 11. Neuroscience. 2021. PMID: 33309967 Free PMC article. Review.
Cited by
-
Design and nonlinear modeling of a sensitive sensor for the measurement of flow in mice.Physiol Meas. 2018 Jul 3;39(7):075002. doi: 10.1088/1361-6579/aacb1b. Physiol Meas. 2018. PMID: 29877866 Free PMC article.
-
Open-loop static and dynamic characteristics of the arterial baroreflex system in rabbits and rats.J Physiol Sci. 2016 Jan;66(1):15-41. doi: 10.1007/s12576-015-0412-5. Epub 2015 Nov 5. J Physiol Sci. 2016. PMID: 26541155 Free PMC article. Review.
-
Nonlinear identification of the total baroreflex arc.Am J Physiol Regul Integr Comp Physiol. 2015 Dec 15;309(12):R1479-89. doi: 10.1152/ajpregu.00278.2015. Epub 2015 Sep 9. Am J Physiol Regul Integr Comp Physiol. 2015. PMID: 26354845 Free PMC article.
-
Investigating bottom-up auditory attention.Front Hum Neurosci. 2014 May 27;8:327. doi: 10.3389/fnhum.2014.00327. eCollection 2014. Front Hum Neurosci. 2014. PMID: 24904367 Free PMC article.
-
Receptive field inference with localized priors.PLoS Comput Biol. 2011 Oct;7(10):e1002219. doi: 10.1371/journal.pcbi.1002219. Epub 2011 Oct 27. PLoS Comput Biol. 2011. PMID: 22046110 Free PMC article.
References
Publication types
MeSH terms
LinkOut - more resources
Other Literature Sources