Comparative nonlinear modeling of renal autoregulation in rats: Volterra approach versus artificial neural networks.

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

SN - 2162-237X

VL - 9

SP - 430

EP - 435

JO - IEEE Transactions on Neural Networks and Learning Systems

JF - IEEE Transactions on Neural Networks and Learning Systems

IS - 3

ER -