Comparative nonlinear modeling of renal autoregulation in rats: Volterra approach versus artificial neural networks.
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Comparative nonlinear modeling of renal autoregulation in rats: Volterra approach versus artificial neural networks. / Chon, K H; Holstein-Rathlou, N H; Marsh, D J; Marmarelis, V Z.
I: IEEE Transactions on Neural Networks, Bind 9, Nr. 3, 1998, s. 430-5.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Comparative nonlinear modeling of renal autoregulation in rats: Volterra approach versus artificial neural networks.
AU - Chon, K H
AU - Holstein-Rathlou, N H
AU - Marsh, D J
AU - Marmarelis, V Z
PY - 1998
Y1 - 1998
N2 - In this paper, feedforward neural networks with two types of activation functions (sigmoidal and polynomial) are utilized for modeling the nonlinear dynamic relation between renal blood pressure and flow data, and their performance is compared to Volterra models obtained by use of the leading kernel estimation method based on Laguerre expansions. The results for the two types of artificial neural networks and the Volterra models are comparable in terms of normalized mean square error (NMSE) of the respective output prediction for independent testing data. However, the Volterra models obtained via the Laguerre expansion technique achieve this prediction NMSE with approximately half the number of free parameters relative to either neural-network model. However, both approaches are deemed effective in modeling nonlinear dynamic systems and their cooperative use is recommended in general.
AB - In this paper, feedforward neural networks with two types of activation functions (sigmoidal and polynomial) are utilized for modeling the nonlinear dynamic relation between renal blood pressure and flow data, and their performance is compared to Volterra models obtained by use of the leading kernel estimation method based on Laguerre expansions. The results for the two types of artificial neural networks and the Volterra models are comparable in terms of normalized mean square error (NMSE) of the respective output prediction for independent testing data. However, the Volterra models obtained via the Laguerre expansion technique achieve this prediction NMSE with approximately half the number of free parameters relative to either neural-network model. However, both approaches are deemed effective in modeling nonlinear dynamic systems and their cooperative use is recommended in general.
U2 - 10.1109/72.668884
DO - 10.1109/72.668884
M3 - Journal article
C2 - 18252466
VL - 9
SP - 430
EP - 435
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
SN - 2162-237X
IS - 3
ER -
ID: 8419767