Abstract
This work shows the design and application of a mixed-mode analog-digital neural network circuit for sensor conditioning applications. The proposed architecture provides a high extension of the linear range for non-linear output sensors as negative temperature coefficient resistors (NTC) or giant magnetoresistive (GMR) angular position sensors, by using analog current-mode circuits with digital 8-bit weight storage. We present an analog current-based neuron model with digital weights, showing its architecture and features. By modifying the algorithm used in off-chip weight fitting, main differences of the electronic architecture, compared to the ideal model, are compensated. A small neural network based on the proposed architecture is applied to improve the output of NTC thermistors and GMR sensors, showing good results. Circuit complexity and performance make these systems suitable to be implemented as on-chip compensation modules.
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Zatorre, G., Medrano, N., Celma, S., Martín-del-Brío, B., Bono, A. (2005). Smart Sensing with Adaptive Analog Circuits. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_57
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DOI: https://doi.org/10.1007/11494669_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
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