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Nonlinear physiological system model...
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Mitsis, Georgios D.,
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Nonlinear physiological system modeling with Laguerre-Volterra networks /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Nonlinear physiological system modeling with Laguerre-Volterra networks // Georgios D Mitsis.
作者:
Mitsis, Georgios D.,
面頁冊數:
1 electronic resource (52 pages)
附註:
Source: Masters Abstracts International, Volume: 64-04.
Contained By:
Masters Abstracts International64-04.
標題:
Biomedical research. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1409648
ISBN:
9780493698175
Nonlinear physiological system modeling with Laguerre-Volterra networks /
Mitsis, Georgios D.,
Nonlinear physiological system modeling with Laguerre-Volterra networks /
Georgios D Mitsis. - 1 electronic resource (52 pages)
Source: Masters Abstracts International, Volume: 64-04.
The study of many important nonlinear systems relies on the use of Volterra models. The use of feedforward artificial neural networks for modeling nonlinear Volterra systems has been recently addressed by means of tapped delay inputs and polynomial activation functions. Previous modeling studies also demonstrated the practical advantages of using Laguerre expansions of the Volterra kernels in order to achieve model compactness and estimation accuracy. The resulting Laguerre expansion technique can be combined with the feedforward ANN utilizing polynomial activation functions in the form of the Laguerre-Volterra network (LVN). The purpose of this work is first to introduce a computationally efficient way in order to train the network parameters and also to propose an alternative form of the LVN suitable for studying systems displaying complex dynamics. Results from applying this modeling approach are presented herein and demonstrate its effectiveness and robustness.
English
ISBN: 9780493698175Subjects--Topical Terms:
3433833
Biomedical research.
Nonlinear physiological system modeling with Laguerre-Volterra networks /
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The study of many important nonlinear systems relies on the use of Volterra models. The use of feedforward artificial neural networks for modeling nonlinear Volterra systems has been recently addressed by means of tapped delay inputs and polynomial activation functions. Previous modeling studies also demonstrated the practical advantages of using Laguerre expansions of the Volterra kernels in order to achieve model compactness and estimation accuracy. The resulting Laguerre expansion technique can be combined with the feedforward ANN utilizing polynomial activation functions in the form of the Laguerre-Volterra network (LVN). The purpose of this work is first to introduce a computationally efficient way in order to train the network parameters and also to propose an alternative form of the LVN suitable for studying systems displaying complex dynamics. Results from applying this modeling approach are presented herein and demonstrate its effectiveness and robustness.
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