Alvarez-Rodríguez, S.; Peña-Lecona, F.G. Artificial Neural Networks with Machine Learning Design for a Polyphasic Encoder. Sensors2023, 23, 8347.
Alvarez-Rodríguez, S.; Peña-Lecona, F.G. Artificial Neural Networks with Machine Learning Design for a Polyphasic Encoder. Sensors 2023, 23, 8347.
Alvarez-Rodríguez, S.; Peña-Lecona, F.G. Artificial Neural Networks with Machine Learning Design for a Polyphasic Encoder. Sensors2023, 23, 8347.
Alvarez-Rodríguez, S.; Peña-Lecona, F.G. Artificial Neural Networks with Machine Learning Design for a Polyphasic Encoder. Sensors 2023, 23, 8347.
Abstract
Artificial neural networks are a powerful tool for managing data that is difficult to process and interpret. This paper presents the study of artificial neural networks for information processing generated by an optical encoder based on the polarization of light. A machine learning technique is proposed to train the neural networks, such that the system can predict with remarkable accuracy the angular position in which the rotating element of the neuro-encoder is located, based on information provided by light’s phase shifting arrangements. The proposed neural designs show excellent performance in small angular intervals, and a methodology is proposed to avoid losing this remarkable characteristic in measurements from 0 to 180o or even to 360o. The neuro-encoder is implemented in simulation stage to obtain performance results. This study can be useful to improve capabilities of resolvers or other polyphasic sensors.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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