A neural flow estimator

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 1995

Documents

DOI

View graph of relations

This paper proposes a new way to estimate the flow in a micromechanical flow channel. A neural network is used to estimate the delay of random temperature fluctuations induced in a fluid. The design and implementation of a hardware efficient neural flow estimator is described. The system is implemented using switched-current technique and is capable of estimating flow in the μl/s range. The neural estimator is built around a multiplierless neural network, containing 96 synaptic weights which are updated using the LMS1-algorithm. An experimental chip has been designed that operates at 5 V with a total current consumption of 2 mA, resulting in a power consumption of 10 mW. The dimensions of the clip core are 3 mm×4.5 mm
Original languageEnglish
Title of host publicationProceedings of the IEEE Instrumentation and Measurement Technology Conference : Integrating Intelligent Instrumentation and Control
PublisherIEEE
Publication date1995
Pages385-385
ISBN (print)07-80-32615-6
DOIs
StatePublished

Conference

ConferenceIEEE Instrumentation and Measurement Technology Conference
CountryUnited States
CityWaltham, MA
Period23/04/9526/04/95
Internet addresshttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=3896

Bibliographical note

Copyright: 1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

CitationsWeb of Science® Times Cited: No match on DOI
Download as:
Download as PDF
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
Word

Download statistics

No data available

ID: 4536398