A neural flow estimator

Ivan Harald Holger Jørgensen; Gudmundur Bogason; Erik Bruun

doi:10.1109/IMTC.1995.515299

A neural flow estimator

Ivan Harald Holger Jørgensen, Gudmundur Bogason, Erik Bruun

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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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 language	English
Title of host publication	Proceedings of the IEEE Instrumentation and Measurement Technology Conference : Integrating Intelligent Instrumentation and Control
Publisher	IEEE
Publication date	1995
Pages	385-385
ISBN (Print)	07-80-32615-6
DOIs	https://doi.org/10.1109/IMTC.1995.515299
Publication status	Published - 1995
Event	IEEE Instrumentation and Measurement Technology Conference: Integrating Intelligent Instrumentation and Control - Waltham, MA, United States Duration: 23 Apr 1995 → 26 Apr 1995 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=3896

Conference

Conference	IEEE Instrumentation and Measurement Technology Conference
Country	United States
City	Waltham, MA
Period	23/04/1995 → 26/04/1995
Internet address	http://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

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Cite this

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title = "A neural flow estimator",

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",

author = "J{\o}rgensen, {Ivan Harald Holger} and Gudmundur Bogason and Erik Bruun",

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; IEEE Instrumentation and Measurement Technology Conference : Integrating Intelligent Instrumentation and Control, IMTC/95 ; Conference date: 23-04-1995 Through 26-04-1995",

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AU - Bogason, Gudmundur

AU - Bruun, Erik

N1 - 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

PY - 1995

Y1 - 1995

N2 - 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

AB - 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

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DO - 10.1109/IMTC.1995.515299

M3 - Article in proceedings

SN - 07-80-32615-6

SP - 385

EP - 385

BT - Proceedings of the IEEE Instrumentation and Measurement Technology Conference

PB - IEEE

T2 - IEEE Instrumentation and Measurement Technology Conference

Y2 - 23 April 1995 through 26 April 1995

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