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

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.