基于最大灵敏度神经网络的温度系统学习控制

2013 12th Mexican International Conference on Artificial Intelligence Pub Date : 2013-11-24 DOI:10.1109/MICAI.2013.19

D. Cabrera-Gaona, L. Torres-Treviño, A. Rodríguez-Liñán

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引用次数: 4

摘要

在嵌入式系统中实现了最大灵敏度神经网络，实现了一个在线机器学习系统，并将其用于小室的温度控制。这是通过使用电位器手动控制温度到不同的设定点，并使用这些值作为神经网络的在线训练数据来实现的。然后，神经网络能够自动调节温度到任意给定的设定值，并具有良好的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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.

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2013 12th Mexican International Conference on Artificial Intelligence

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