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Developing smart micromachined transducers using feedforward neural networks: a system identification and control perspective

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3 Author(s)
E. I. Gaura ; Coventry Univ., UK ; R. J. Rider ; N. Steele

Describes some possible applications of feedforward neural networks in the sensorial field. The subject of the research was a micromachined acceleration sensor, with a capacitive type of pick-off. Static sensor identification (based on measurement results) and dynamic identification (based on the mechanical model of the sensor) was performed with a view to develop, neural, open- and closed-loop transducers with improved performance characteristics. Measurement results are presented for the open loop, neural transducer, which was implemented in hardware. Two closed-loop structures were proposed which used static and/or dynamic networks. The performance of these transducers was assessed based on simulation results. All neural network controlled transducers showed an extended measurement range compared to the off-the-shelf sensors and, in the closed loop designs, the latch-up condition was eliminated

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Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on  (Volume:4 )

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