A neural flow estimator

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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
Publication date1995
ISBN (Print)07-80-32615-6
Publication statusPublished - 1995
EventIEEE Instrumentation and Measurement Technology Conference: Integrating Intelligent Instrumentation and Control - Waltham, MA, United States
Duration: 23 Apr 199526 Apr 1995


ConferenceIEEE Instrumentation and Measurement Technology Conference
CountryUnited States
CityWaltham, MA
Internet address

Bibliographical note

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