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Temperature Control based on Improved Particle Swarm Optimitization to the Adhesive Preparation Processing

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成果类型:
期刊论文、会议论文
作者:
Zhou Guo-xiong*;Li Lin;Tang Jun
通讯作者:
Zhou Guo-xiong
作者机构:
[Zhou Guo-xiong; Li Lin] Cent South Univ Forestry & Technol, Sch Elect & Informat Engn, Changsha 410004, Hunan, Peoples R China.
[Tang Jun] Hunan Coll Informat, Dept Econ & Management, Changsha 410200, Peoples R China.
通讯机构:
[Zhou Guo-xiong] C
Cent South Univ Forestry & Technol, Sch Elect & Informat Engn, Changsha 410004, Hunan, Peoples R China.
语种:
英文
关键词:
Improved particle swarm optimization;Temperature;Variation
期刊:
Advanced Materials Research
ISSN:
1022-6680
年:
2012
卷:
516-517
页码:
224-227
会议名称:
1st International Conference on Energy and Environmental Protection (ICEEP 2012)
会议论文集名称:
Advanced Materials Research
会议时间:
JUN 23-24, 2012
会议地点:
Hohhot, PEOPLES R CHINA
会议主办单位:
[Zhou Guo-xiong;Li Lin] Cent South Univ Forestry & Technol, Sch Elect & Informat Engn, Changsha 410004, Hunan, Peoples R China.^[Tang Jun] Hunan Coll Informat, Dept Econ & Management, Changsha 410200, Peoples R China.
会议赞助商:
Inner Mongolia Univ
主编:
Yan, JY Zhou, CC Liao, R Wang, JW
出版地:
KREUZSTRASSE 10, 8635 DURNTEN-ZURICH, SWITZERLAND
出版者:
TRANS TECH PUBLICATIONS LTD
ISBN:
978-3-03785-415-0
基金类别:
Central South University of Forestry and Technology Research Fund focus on talent projects of china [1040219]; Youth Science Foundation of Central South University of Forestry and Technology [QJ2010011B]
机构署名:
本校为第一且通讯机构
院系归属:
计算机与信息工程学院
摘要:
In view of the characteristics of the preparation of an adhesive process which is lengthy, nonlinear, time-varying, big inertia and pure delay, A Proportional-Integral-Derivative (PID) algorithm is proposed based on the improved particle swarm optimization. Because there are drawbacks in the design of PID controller, an improved particle swarm optimization which takes into account a number of performances is propesed to modified parameters of PID controllers. In which the variation of genetic algorithm is introduced to the standard particle swarm optimization algorithm, which can avoid local m...

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