Abstract
In induction home appliances, recipient size estimation is very important for the adjustment of the power that the hob must supply to the cooking recipient. Conventional techniques first calculate a simple R–L equivalent circuit from voltage and current waveforms in the heating inductor and then estimate physical parameters (such as recipient size) by regression. In this paper, a new technique is proposed for recipient size estimation, based on spectral analysis and artificial neural networks (ANN), which, for two reasons, is more accurate than current procedures: (i) The new technique performs a direct estimation of recipient size from voltage and current, without needing to compute an intermediate electrical equivalent circuit (which in fact only represents a rough approximation), and (ii) due to their nonlinear modeling capabilities, ANNs are more appropriate than regression for this problem. By using a database of cooking recipients, our procedure provides an accuracy of 85–90 %, outperforming the 58 % of conventional techniques (70 % including an additional sensor). The new technique has been implemented and verified by using a commercial embedded processor, similar to those included in current domestic induction home appliances. It could be built in by manufacturers at no extra cost as it requires no additional sensors and makes use of computational resources integrated into the microcontroller or the digital signal processor that controls the home appliance.
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Acknowledgments
This work has been funded by the following projects: MINECO TEC2013-42937-R, MINECO TIN2013-45312-R, CSD 2009-00046 (CONSOLIDER–INGENIO), and JIUZ-2013-TEC-05, and supported by the GEPM and CVLab research groups. The authors also wish to thank the company Bosch und Siemens Hausgeräte (BSH group) for its support through a partnership between BSH and the University of Zaragoza.
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Bono-Nuez, A., Bernal-Ruíz, C., Martín-del-Brío, B. et al. Recipient size estimation for induction heating home appliances based on artificial neural networks. Neural Comput & Applic 28, 3197–3207 (2017). https://doi.org/10.1007/s00521-016-2227-6
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DOI: https://doi.org/10.1007/s00521-016-2227-6