A neural flow estimator

Ivan Harald Holger Jørgensen, Gudmundur Bogason, Erik Bruun

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    Abstract

    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
    Publication statusPublished - 1995
    Event1995 IEEE Instrumentation and Measurement Technology Conference - Waltham, United States
    Duration: 23 Apr 199526 Apr 1995
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=3896

    Conference

    Conference1995 IEEE Instrumentation and Measurement Technology Conference
    Country/TerritoryUnited States
    CityWaltham
    Period23/04/199526/04/1995
    Internet address

    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

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