参考文献/References:
[1] 千红涛.环境试验箱的可适应产品平台设计方法与应用研究[D].徐州:中国矿业大学,2015.[2] 王洪航,褚建新,张江明,等.基于模糊控制的温湿度试验系统的研究[J].化工自动化及仪表,2015,42(03):300-302.
[3] 申中鸿,杨林,刘群兴,等.高低温试验箱温湿度控制技术研究[J].伺服控制,2013(10):55-57.
[4] 王瑾,袁战军,李小斌.智能温度控制系统的设计[J].自动化与仪器仪表,2016(08):175-177.
[5] 张玲,张文苑,郑恩让.一种模糊解耦控制系统的设计与仿真研究[J].计算机仿真,2010,27(08):118-121.
[6] RAN Maopeng,WANG Qing,HOU Delong,et al. Backste- pping design of missile guidance and control based on adaptive fuzzy sliding mode control[J].Chinese Journal of Aeronautics,2014,27(03):634-642.
[7] Direct adaptive neural control for stabilization of nonlinear time-delay systems[J].Science China(Information Sciences), 2010,53(04):800-812.?
[8] 陈文科,唐黄正.一种蒸汽养护窑温湿度模糊解耦控制系统仿真研究[J].科技创新与应用,2017(36):12-13.
[9] 孙碧.模糊自适应控制在温湿度系统中的应用与研究[D].广州:华南理工大学,2015.
[10] 彭基伟,吕文华,行鸿彦,等.基于改进GA-BP神经网络的湿度传感器的温度补偿[J].仪器仪表学报,2013,34(01): 153-160.
[11] 张宁子,杨泽林,俞晓红.基于模糊PID温湿度解耦控制器的仿真与结果分析[J].宁夏工程技术,2013,12(01): 14-17.
[12] 赵静.精馏塔温度模糊解耦控制系统的研究[J].工业仪表与自动化装置,2013(06):50-54.
[13] 刘珑龙,刘雪锋,周西龙.基于模糊神经网络的多变量系统的解耦方法[J].中国海洋大学学报(自然科学版),2013, 43(02):99-104.
[14] Zhisheng NI, Mingyan WANG. Research on the Fuzzy Neural Network PID Control of Load Simulator Based on Friction Torque Compensation[C].Intelligent Human- Machine Systems and Cybernetics(IHMSC),2014 Sixth International Conference on,2014.
[15] Wei ZHANG,Hong MA,Simon X YANG.A neuro-fuzzy decoupling approach for real-time drying room control in meat manufacturing[J].Expert Systems With Applications, 2015, 42(3):
相似文献/References:
[1]刘 俊.基于搜寻者优化算法的PID神经网络解耦控制[J].工业仪表与自动化装置,2015,(05):97.
LIU Jun.PID neural network decoupling control based on seeker optimization algorithm[J].Industrial Instrumentation & Automation,2015,(05):97.
[2]杨 智,陈雨琴,杨晓光.继电自整定PID温湿度解耦控制系统设计与实现[J].工业仪表与自动化装置,2015,(06):70.
YANG Zhi,CHEN Yuqin,YANG Xiaoguang.Design and implementation of temperature and humidity decoupling control system based on relay self-tuning PID[J].Industrial Instrumentation & Automation,2015,(05):70.
[3]杜青青.基于最小二乘支持向量机逆系统方法应用研究[J].工业仪表与自动化装置,2019,(05):122.[doi:1000-0682(2019)05-0000-00]
DU Qingqing.Application of inverse system method based on least squares support vector machine[J].Industrial Instrumentation & Automation,2019,(05):122.[doi:1000-0682(2019)05-0000-00]