In this work, a single-shot direct inverse compensation procedure based on neural networks is proposed, with application to micromachined accelerometers. Compensation was first considered from an empirical viewpoint to determine whether or not some kind of relationship exists between the severity of different nonlinearities and the complexity of the network required to control such nonlinearities. The procedure was then validated by applying direct inverse control to the measured static characteristic of a micromachined acceleration sensing element.
CITATION STYLE
Gaura, E., Rider, R., & Steele, N. (2000). Neural network based compensation of micromachined accelerometers for static and low frequency applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1821, pp. 534–542). Springer Verlag. https://doi.org/10.1007/3-540-45049-1_63
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