Control by Learning in a Temperature System using a Maximum Sensibility Neural Network

被引：0

作者：

Cabrera-Gaonal, D. ^{[1
]}

Torres-Trevino, Luis M. ^{[1
]}

Rodriguez-Linan, Angel ^{[1
]}

机构：

[1] Univ Autonoma Nuevo Leon, FIME, San Nicolas De Los Garza 66451, Nuevo Leon, Mexico

来源：

2013 12TH MEXICAN INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (MICAI 2013) | 2013年

关键词：

Neural networks; on-line learning; Control by learning;

D O I：

10.1109/MICAI.2013.19

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 setpoint with a good performance.

引用

页码：109 / 113

页数：5

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