| 采用混沌粒子群优化算法的水质模型参数辨识 |
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| 引用本文： | 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). |
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| 作者姓名： | 袁君 陈贝 朱光灿 |
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| 作者单位： | 东南大学能源与环境学院,南京,210096 |
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| 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 |
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| 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.
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| 关 键 词： | 水质模型 参数辨识 Logistic混沌 粒子群优化 |
Parameter identification of water quality model based on chaotic particle swarm optimization |
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| 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). |
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| Authors: | Yuan Jun Chen Bei Zhu Guangcan |
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| Institution: | Yuan Jun Chen Bei Zhu Guangcan(School of Energy , Environment,Southeast University,Nanjing 210096,China) |
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| 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 clas... |
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| Keywords: | water quality model parameter identification logistic chaos particle swarm optimization |
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