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Math. Comput. Appl. 2011, 16(3), 702-711; doi:10.3390/mca16030702

Simulation of Hemodynamics in a Graft-To-Vein Anastomoses by Adaptive Neuro-Fuzzy Based Modeling

1
Department of Genetics and Bioengineering, Turkey
2
Department of Environmental Engineering Fatih University, Büyükçekmece, 34500, Istanbul, Turkey
*
Author to whom correspondence should be addressed.
Published: 1 December 2011
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Abstract

A new methodology for simulating the flow field inside an arteriovenous (AV) graft to vein anastomoses by the adaptive neuro fuzzy inference system (ANFIS) is presented in this study. For determining the optimal AV graft angle, an ANFIS-based model of neuro fuzzy-graft-vein (NF-GVEIN) is proposed. Therefore engineering design of the graft can be supported. The advantage of this neuro-fuzzy hybrid model is that it does not require the model structure to be known a priori, in contrast to most of the modeling techniques. A case study with real experimental data was carried out. NFGVEIN was optimized by means of selection of the algorithm among 34 ANFIS algorithms by terms of minimal error. The optimal neural network structure was determined. The optimal AV graft angle causing the least turbulence was obtained. The simulation results showed that this model is feasible for forecasting of finding the optimal AV graft angle inside AV graft to vein anastomoses with different flow rates.
Keywords: Neural Networks; ANFIS; Hemodynamics; Graft Design Neural Networks; ANFIS; Hemodynamics; Graft Design
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Arslan, N.; Karaca, F. Simulation of Hemodynamics in a Graft-To-Vein Anastomoses by Adaptive Neuro-Fuzzy Based Modeling. Math. Comput. Appl. 2011, 16, 702-711.

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