[1]袁君,陈贝,朱光灿.采用混沌粒子群优化算法的水质模型参数辨识[J].东南大学学报(自然科学版),2009,39(5):1018-1022.[doi:10.3969/j.issn.1001-0505.2009.05.030]
 Yuan Jun,Chen Bei,Zhu Guangcan.Parameter identification of water quality model based on chaotic particle swarm optimization[J].Journal of Southeast University (Natural Science Edition),2009,39(5):1018-1022.[doi:10.3969/j.issn.1001-0505.2009.05.030]
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采用混沌粒子群优化算法的水质模型参数辨识()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
39
期数:
2009年第5期
页码:
1018-1022
栏目:
环境科学与工程
出版日期:
2009-09-20

文章信息/Info

Title:
Parameter identification of water quality model based on chaotic particle swarm optimization
作者:
袁君 陈贝 朱光灿
东南大学能源与环境学院, 南京 210096
Author(s):
Yuan Jun Chen Bei Zhu Guangcan
School of Energy and Environment, Southeast University, Nanjing 210096, China
关键词:
水质模型 参数辨识 Logistic混沌 粒子群优化
Keywords:
water quality model parameter identification logistic chaos particle swarm optimization
分类号:
X11
DOI:
10.3969/j.issn.1001-0505.2009.05.030
摘要:
提出了一种新的适用于水质模型参数辨识的混沌粒子群优化(LCPSO)算法.与粒子群优化(PSO)算法相比,该算法将Logistic混沌搜索嵌入到PSO算法中,利用混沌变量产生初始粒群,并对子代部分粒子群体进行微小扰动,随着搜索过程的深入逐步调整扰动幅度,以克服PSO算法的早熟、易陷入局部极值等固有缺陷.采用标准测试函数,将该算法与遗传算法(GA)和PSO算法进行比较,证明了其收敛速度和寻优能力的优越性.采用实测水质数据,将LCPSO算法应用于具有一定工程价值和复杂程度的Dobbins-Camp BOD-DO水质模型的参数辨识.结果显示,所得水质数据与实测值误差平方和仅为0.150 3,且相对误差在±0.2%范围内,故该算法可为水质模型的参数辨识提供一条新的途径.
Abstract:
A novel method of the logistic chaotic particle swarm optimization(LCPSO)is presented for the parameter identification of the water quality model on the basis of standard particle swarm optimization(PSO)algorithm. The LCPSO generates the initial particles and adds a small disturbance to the partial particles of child generation group by using the chaos variable and the disturbance amplitude is adjusted little by little as the search goes on to escape from local best solutions. The simulation results of classic functions indicate that the LCPSO algorithm solves the defects of genetic algorithm(GA)and the PSO algorithm which are apt to trap in local minimums and premature problem, and has great advantage of convergence property. Based on experimental water quality data, the above approach is applied to the parameter identification of the Dobbins-Camp BOD-DO model with considerable practical significance and complexity. The results of calculation examples demonstrate that the error square sum is only 0.150 3, while the relative error curve fluctuates within the range of ±0.2%. It is concluded that the LCPSO can be applied to the parameter identification of water quality model as a new approach.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介: 袁君(1986—),女,硕士生; 朱光灿(联系人),男,博士,副教授,gc-zhu@seu.edu.cn.
基金项目: 东南大学优秀青年教师教学科研资助计划资助项目.
引文格式: 袁君,陈贝,朱光灿.采用混沌粒子群优化算法的水质模型参数辨识[J].东南大学学报:自然科学版,2009,39(5):1018-1020. [doi:10.3969/j.issn.1001-0505.2009.05.030]
更新日期/Last Update: 2009-09-20