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 |
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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 | |
Publication status | Published - 1995 |
Event | 1995 IEEE Instrumentation and Measurement Technology Conference - Waltham, United States Duration: 23 Apr 1995 → 26 Apr 1995 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=3896 |
Conference
Conference | 1995 IEEE Instrumentation and Measurement Technology Conference |
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Country/Territory | United States |
City | Waltham |
Period | 23/04/1995 → 26/04/1995 |
Internet address |