The Applications of Fuzzy Control and Neural Network to Fieldbus Control System

Abstract

Abstract:

The scientific inevitability of modern industry computer control system progressing f rom the information separation to the information share and the significance of the birth of fieldbus control system(FCS) have been analysed in detail from point of view of information science, and the w ay and the affection of applying fuzzy control and neural network to FCS have been explored.Furthermore, new definition and functions of intelligent inst rument are proposed with the example of intelligent fuzzy instrument.

Key words: FCS, fuzzy control, neural network, intelligent instrument

CLC Number:

Zhou Shangming, Liu Dingsheng. The Applications of Fuzzy Control and Neural Network to Fieldbus Control System[J]. , 2000, 17(2): 81-88.

References

1 李强.现场总线及其发展趋势.电子与自动化仪表, 1996,(6)

2 吴新垣.信息融合及其智能化.软件开发与应用, 1996,(5)

3 M Rao et al.Sof t Sensors for Quality Prediction in Batch Chemical Pulping Processes.In :Proc Int Sym on Intell Contr.USA, 1993

4 L E Hollow ay.The Challenge of Intelligent Sensing for Monitoring and Control.In :Proc Int Sym on Int ell Contr.USA, 1993

5 M Siebert et al.Interworking of Fielddevices.In :IFAC Intell Component s and Inst ru for control and Appli.Hungary, 1994

6 Fieldbus Foundation.现场总线基金会及其基金会现场总线技术的现状和发展, 见`97 北京国际现场总线技术交流会.1997

7 裘玉瑞等.我国“ 现场总线” 发展的策略建议.见:`97 北京国际现场总线技术交流会.1997

8 刘铁椎.现场总线在企业综合自动化系统中的作用和地位.见:`97 北京国际现场总线技术交流会.1997

9 钟霖田.DCS 的现状与发展趋势.见:席裕庚主编.工业过程模型化及控制.上海:上海交通大学出版社, 1994

10 刘宝义.过程工业综合自动化的单元技术进展.微计算机信息, 1996, 12(6)

11 伊红卫.现场总线技术标准及发展.计算机世界, 1997

12 T Blevins et al.How Fieldbus Can influence Your Next Project.control Eng/ sept,1996

13 G Mort in.Sof t Sensors.C &I Jan, 1997

14 M Goodw in.Neural Processing Set to Boost Sensor Technology.SR, 1995, 15(4)

15 E T Pow ner et al.From Basic Sensors to Int elligent Sensors :Definitions and Examples.SR, 1995, 15(4)

16 周尚明等.现代工业生产过程的智能控制与智能自动化.计算机世界, 1997,(17)

17 S M Zhou.A new approach to fuzzy modeling based on recurrent neuralnetwork for fuzzy dynamic systems.Vol.K:39 ～ 44 ;Proceedings of the 14th World Congress of International Federation of Aut omatic control.PERGAMON.Elsevier S cience.1999

[1]	YU Wenxin, LI Kai, ZHOU Mingtuo, LI Jian, YANG Yang. Adaptive neural network for 3D channel amplitude prediction [J]. Journal of University of Chinese Academy of Sciences, 2021, 38(5): 678-686.
[2]	QIAN Huan, XIE Zhuochen, LIANG Xuwen. Single-event upsets fault tolerance of convolutional neural networks based on dropout algorithm [J]. Journal of University of Chinese Academy of Sciences, 2021, 38(5): 712-719.
[3]	JI Yufeng, ZHENG Min, TAN Chong, LIU Hong. Combination algorithm of spectrum sensing in vehicle network based on neural network [J]. Journal of University of Chinese Academy of Sciences, 2021, 38(4): 549-556.
[4]	HU Lina, GAO Shenghua. Hierarchical label-guided human parsing [J]. , 2020, 37(6): 820-827.
[5]	FENG Qixin, YANG Liao, WANG Weisheng, CHEN Tao, HUANG Shuangyan. CNN remote sensing crop classification based on time series spectral reconstruction [J]. , 2020, 37(5): 619-628.
[6]	WANG Hong, XU Youyu, TAN Chong, LIU Hong, ZHENG Min. Information fusion algorithm based on improved particle swarm BP neural network in WSN [J]. , 2020, 37(5): 673-680.
[7]	TANG Siqi, HAN Congying, GUO Tiande. Point matching algorithm based on machine learning method [J]. , 2020, 37(4): 450-457.
[8]	SUN Mingkun, LIANG Lingy, WANG Han, HE Wei, ZHAO Luyang. Facial landmark detection based on cascade convolutional neural network [J]. , 2020, 37(4): 562-569.
[9]	XUE Jianwei, TANG Liang, BU Zhiyong. Cooperative spectrum sensing based on neural network in cognitive radio [J]. , 2020, 37(3): 379-386.
[10]	SONG Han, YANG Weitun, GENG Xiurui, ZHAO Yongchao. Hyperspectral image classification based on convolutional neural network and active learning algorithm [J]. , 2020, 37(2): 169-176.
[11]	WANG Huijun, ZHAO Guiping, LI Liang, ZHANG Wei, QI Rong, LIU Jun. TOC prediction model for muddy source rocks based on convolutional neural network (CNN): a case study of the Hangjinqi area of the Ordos Basin [J]. , 2020, 37(1): 103-112.
[12]	SONG Xuming, SHEN Yifei, SHI Yuanming. Intelligent mobile edge network caching based on deep learning [J]. , 2020, 37(1): 128-135.
[13]	WANG Jianfei, ZHANG Weiqiang, LIU Jia. Scene-independent sound event detection based on multi-state transition model [J]. , 2019, 36(2): 218-225.
[14]	ZHU Sijie, LEI Bin, WU Yirong. Automatic color correction for remote sensing optical image based on dense convolutional networks [J]. , 2019, 36(1): 93-100.
[15]	WANG Chunfeng, SU Li, HUANG Qingming. Spatio-temporal-fused no-reference video quality assessment based on convolutional neural network [J]. , 2018, 35(4): 544-549.