On the role of machine learning algorithms in developing MEMS components

Author

Asmar, Daniel ; Moussa, Medhat ; Zelek, John

Author_Institution

Sch. of Eng., Univ. of Guelph, Canada

fYear

2003

fDate

20-23 July 2003

Firstpage

108

Lastpage

113

Abstract

This paper provides a review of several significant applications of machine learning tools in the development of MEMS components and devices. Four topics are covered, two represent traditional applications of artificial neural networks, drag reduction and reliability forecasting, and two are non-traditional applications, namely model reduction and MEMS based neurocomputing.

Keywords

Galerkin method; computer network reliability; drag reduction; flow control; learning (artificial intelligence); microactuators; micromechanical devices; micromechanical resonators; neural nets; pattern recognition; reduced order systems; shear turbulence; MEMS based neurocomputing; MEMS components; MEMS devices; artificial neural networks; drag reduction; machine learning algorithms; model reduction; nontraditional applications; reliability forecasting; Artificial neural networks; Machine learning algorithms; Microactuators; Microelectromechanical devices; Micromechanical devices; Neural networks; Stress control; Stress measurement; Thermal sensors; Thermal stresses;

fLanguage

English

Publisher

ieee

Conference_Titel

MEMS, NANO and Smart Systems, 2003. Proceedings. International Conference on

Print_ISBN

0-7695-1947-4

Type

conf

DOI

10.1109/ICMENS.2003.1221975

Filename

1221975

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1884575