DocumentCode :
2694944
Title :
Control of left ventricular assist device using artificial neural network
Author :
Kim, Sanghyun ; Kim, Hunmo ; Ryu, Jungwoo ; Chung, Sungtaek
Author_Institution :
Coll. of Med., Yonsei Univ., Seoul, South Korea
Volume :
3
fYear :
1997
fDate :
30 Oct-2 Nov 1997
Firstpage :
1363
Abstract :
Presents the neural network identification and control of a highly complicated nonlinear Left Ventricular Assist Device (LVAD) system with a pneumatically driven mock circulation system. Generally an LVAD system needs to compensate for nonlinearities. Hence, it is necessary to apply high performance control techniques. Fortunately, a neural network can be applied to the control of a nonlinear dynamic system by utilizing its learning capability. Here, the authors identify an LVAD system with Neural Network Identification (NNI). Once the NNI has learned the dynamic model of the LVAD system, another network, called Neural Network Controller (NNC), is designed for control of the LVAD system. The ability and effectiveness of identifying and controlling an LVAD system using the proposed algorithm is demonstrated by computer simulation
Keywords :
artificial organs; biocontrol; cardiology; digital simulation; identification; neurocontrollers; artificial neural network; dynamic model; high performance control techniques; learning capability; left ventricular assist device control; neural network controller; nonlinear dynamic system; nonlinearities compensation; pneumatically driven mock circulation system; Artificial neural networks; Biological neural networks; Blood; Control systems; Medical control systems; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Predictive models; System testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
ISSN :
1094-687X
Print_ISBN :
0-7803-4262-3
Type :
conf
DOI :
10.1109/IEMBS.1997.756630
Filename :
756630
Link To Document :
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