The development of a new angioplasty balloon is a time consuming process. This thesis aims at reducing the amount of time and materials spent on the experimental stage of the development of new angioplasty balloons. This can be achieved by building a nonlinear neural network model of the balloon forming process and implementing an off-line cycle-to-cycle controller. The controller can learn from the previous experiments and provide better input parameters for improving the quality of the next balloons formed in the process. It is shown in the experimental test results that the neural network model can provide accurate estimates of the process outputs. The neural network model combined with a cycle-to-cycle control strategy has the potential to replace the trial-and-error approach to balloon development that is commonly applied today.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.112564 |
| Date | January 2008 |
| Creators | Chen, Yan, 1982- |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Master of Engineering (Department of Electrical and Computer Engineering.) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 002712157, proquestno: AAIMR51454, Theses scanned by UMI/ProQuest. |
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