| PSO-RBF在大坝变形监测中的应用 |
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| 引用本文： | 吕蓓蓓,杨远斐.PSO-RBF在大坝变形监测中的应用[J].水电能源科学,2012,30(8):77-79. |
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| 作者姓名： | 吕蓓蓓 杨远斐 |
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| 作者单位： | 河海大学水利水电学院,江苏南京210098;河海大学水资源高效利用与工程安全国家工程研究中心,江苏南京210098 |
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| 基金项目： | 国家自然科学基金资助项目（50909041,51079046,51079086,51139001）；河海大学水文水资源与水利工程科学国家重点实验室专项基金资助项目（2009586012,2009586912,2010585212） |
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| 摘 要： | 针对传统径向基神经网络(RBF)在大坝安全监测应用中易陷入局部最优及预测精度不高的问题,引入粒子群算法(PSO),对输入的大坝安全监测数据进行初步的聚类处理,找出初步聚类中心后令其为PSO的初值,根据运算法则更新初值以寻求适合训练数据的最优基函数中心。以小湾大坝为例,应用Matlab仿真模拟计算了大坝变形量,结果表明PSO-RBF与传统RBF的拟合效果都很好,PSO-RBF预测准确度更高。
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| 关 键 词： | 大坝安全监测 聚类算法 径向基函数神经网络 粒子群算法 小湾大坝 |
Application of PSO-RBF in Dam Deformation Monitoring |
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| LV Beibei and Yang Yuanfei.Application of PSO-RBF in Dam Deformation Monitoring[J].International Journal Hydroelectric Energy,2012,30(8):77-79. |
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| Authors: | LV Beibei and Yang Yuanfei |
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| Affiliation: | College of Water Conservancy and Hydropower, Hohai University, Nanjing 210098, China; National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China;College of Water Conservancy and Hydropower, Hohai University, Nanjing 210098, China; National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China |
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| Abstract: | The standard RBF neural network is easy to jump into local optimum and has low forecasting accuracy for dam safety monitoring.The particle swarm algorithm is introduced to cluster the dam monitoring data and find the clustering centers,which can be defined as initial values of PSO.Then the initial values are updated to find the optimal basis function center of training data based on the operational rule of PSO.Taking Xiaowan dam for an example,the dam deformation monitoring is simulated with Matlab software.The results show that both of the PSO-RBF and standard RBF have good fitting effect while PSO-RBF has higher prediction accuracy. |
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| Keywords: | dam safety monitoring clustering algorithm radial basis function neural network particle swarm optimization Xiaowan dam |
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