Simulation of hemodynamics in a graft-to-vein anastomoses by adaptive neuro-fuzzy based modeling

Nurullah Arslan, Ferhat Karaca

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)702-711
Number of pages10
JournalMathematical and Computational Applications
Issue number3
Publication statusPublished - Apr 7 2011
Externally publishedYes


  • Graft design
  • Hemodynamics
  • Neural networks

ASJC Scopus subject areas

  • Engineering(all)
  • Computational Mathematics
  • Applied Mathematics

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