引用本文: 王伟, 沈振中. 大坝统计预警模型的改进粒子群耦合方法[J]. 武汉大学学报 ( 信息科学版), 2009, 34(8): 987-991.
WANG Wei, SHEN Zhenzhong. Statistical Early Warning Model for Dam Based on Improved Particle Swarm Coupled Method[J]. Geomatics and Information Science of Wuhan University, 2009, 34(8): 987-991.
 Citation: WANG Wei, SHEN Zhenzhong. Statistical Early Warning Model for Dam Based on Improved Particle Swarm Coupled Method[J]. Geomatics and Information Science of Wuhan University, 2009, 34(8): 987-991.

## Statistical Early Warning Model for Dam Based on Improved Particle Swarm Coupled Method

• 摘要: 提出了一种新的自适应策略,并与模拟退火算法相结合,建立了基于模拟退火-自适应粒子群耦合方法的大坝统计预警模型。通过工程算例的研究表明,该方法能够提高粒子群算法的收敛性能,易跳出局部最小,算法收敛速度快。基于该方法的预警模型与最小二乘法相比,预报精度较高,预警评价结果与大坝的实际运行情况相吻合。

Abstract: Because the dam safety influencing indexes are complex,sometimes the results are bad when the statistical model is applied to early warning evaluation for dam. Suppose regression coefficients is transformed to the linear programming question. The coefficients of multi-statistic regression can be determined by the particle swarm optimization algorithm. But when the PSO algorithm was applied to high dimension space optimization question,the convergence rate would be slow and the calculations could easily fall into local extreme points. In order to overcome these shortcomings,the PSO is improved,and a new self-adapting strategy is proposed. The coupled method combines a new self-adapting strategy and simulated annealing algorithm,and the statistical early warning model for dam is based on it (SA-APSOR). Applications in practical engineering show that this method can improve the convergence ability of PSO,and can avoid the algorithm falling into the local extreme points. Hence,the convergence rate of this method is quick. Furthermore,compared with the traditional least square regression,the forecast precision of this model based on SA-APSOR is high and its early warning evaluation results almost correspond with the practice operating condition.

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