Design and Implementation of Small Smart Fuzzy Neural Network Temperature-control System
摘 要
提出并设计了一种基于热电制冷器(TEC)的小型快速模糊神经网络温控系统。该系统的硬件由温度传感器、TEC及其驱动器和微控制器组成;系统的软件设计采用模糊推理系统和神经网络相结合, 通过控制脉冲宽度调制(PWM)发生器输出合适的波形, 驱动TEC制冷或者制热。仿真试验结果表明: 该系统较传统的比例-积分-微分(PID)温度控制器具有更加良好的控制效果。
Abstract
A fuzzy neural network temperature-control system based on thermoelectric cooler (TEC) was proposed and designed. The hardware of this system consisted of temperature sensor, microcontroller, thermoelectric cooler and its driver. The software of this system adopted fuzzy control algorithm and neural network algorithm. The output of the fuzzy neural network (FNN) was loaded into the MCU to control the PWM waveform generator, which could generate suitable waveform to drive the TEC. Simulations of the system show that the proposed FNN temperature-control system had better performance than the conventional PID controller.
中图分类号 TP183 TP273
所属栏目 试验与研究
基金项目 国际合作资助项目(BZ2010060); 教育部博士点(博导类)专项基金资助项目(20093218110024); 中国博士后基金资助项目(2013M531346); 江苏省博士后基金资助项目(1201002B); 江苏省光谱成像与智能感知重点实验室开放基金资助项目(30920130122003)
收稿日期 2013/9/20
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备注张林(1985-), 男, 博士。
引用该论文: ZHANG Lin,ZHAO Zhi-min,ZHU Xing-yue,WANG Le-xin,YU Yin-shan. Design and Implementation of Small Smart Fuzzy Neural Network Temperature-control System[J]. Physical Testing and Chemical Analysis part A:Physical Testing, 2014, 50(12): 878~881
张林,赵志敏,朱星玥,王乐新,于银山. 小型快速模糊神经网络温控系统的设计与实现[J]. 理化检验-物理分册, 2014, 50(12): 878~881
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【4】JUANG C F. A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms[J]. Ieee Transactions on Fuzzy Systems,2002,10(2):155-170.
【5】LIN C J,CHIN C C. Prediction and identification using wavelet-based recurrent fuzzy neural networks[J]. Ieee Transactions on Systems, Man, and Cybernetics Part B:Cybernetics,2004,34(5):2144-2154.
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【11】周帅, 张彤, 崔一平. 一种TEC温度控制模块的电路设计与仿真[J]. 电子器件, 2008, 31(5): 1591-1594.
【12】郭经纬, 吴志明, 吕坚, 等. 基于ADN8831的高性能温度控制系统设计[J]. 传感器与微系统, 2008, 27(2): 103-105.
【13】李蒙, 李洪涛, 吴益文, 等. 热电材料的应用、研究及性能测试进展[J]. 理化检验-物理分册, 2013, 49(1): 27-33.
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