Learning of a backpropagation neural network to tune a fuzzy control of a thermal system

R. Urbieta Parrazales, M. Ramírez, S. Osvaldo Espinosa, J. Elena Aguilar, A. De Luca
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Abstract

This regular paper describes three important aspects: design, simulation and implementation of neuro-fuzzy control applied to the temperature variable of a thermal system with a range of 25/spl deg/C to 75/spl deg/C and resolution of 0.01%. In the design were found the membership functions and the fuzzy rules base already optimized of the fuzzy controller by means of a backpropagation neural network trained to 120 learning cycles. The simulation presents basic tables of the fuzzy controller obtained by the neural network. The implementation of the program of the fuzzy controller was effected using a 486 PC with conversion card A/D and of 8-bit port output.
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学习反向传播神经网络调节热系统的模糊控制
本文介绍了三个重要方面:应用于热系统温度变量的神经模糊控制的设计、仿真和实现,温度范围为25/spl℃至75/spl℃,分辨率为0.01%。在设计中,通过训练到120个学习周期的反向传播神经网络,找到了模糊控制器的隶属函数和模糊规则库。仿真给出了由神经网络得到的模糊控制器的基本表。采用带a /D转换卡和8位端口输出的486 PC机实现模糊控制器程序。
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