Title :
Control by Learning in a Temperature System Using a Maximum Sensibility Neural Network
Author :
Cabrera-Gaona, D. ; Trevino, Luis M. Torres ; Rodriguez-Linan, Angel
Author_Institution :
FIME, Univ. Autonoma de Nuevo Leon, San Nicolas de los Garza, Mexico
Abstract :
A maximum sensibility neural network is implemented in an embedded system to make an online machine learning system, which is used to control the temperature of a small chamber. This is made by manually controlling the temperature to different set-points with a potentiometer, and using these values as an online training data for the neural network. Then the neural network is able to automatically adjust the temperature to any given set point with a good performance.
Keywords :
embedded systems; learning (artificial intelligence); neurocontrollers; potentiometers; temperature control; control-by-learning; embedded system; maximum sensibility neural network; online machine learning system; potentiometer; temperature control system; Biological neural networks; Neurons; Temperature control; Temperature measurement; Temperature sensors; Training; Neural networks; control by learning; on-line learning;
Conference_Titel :
Artificial Intelligence (MICAI), 2013 12th Mexican International Conference on
Conference_Location :
Mexico City
Print_ISBN :
978-1-4799-2604-6
DOI :
10.1109/MICAI.2013.19
