| 小生境蚁群-BP神经网络在大坝变形监测中的应用 |
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| 引用本文： | 魏〓玮,陈〓晨,邵晨飞,许焱鑫,刘峻杉.小生境蚁群-BP神经网络在大坝变形监测中的应用[J].水电能源科学,2014,32(8):85-87. |
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| 作者姓名： | 魏〓玮 陈〓晨 邵晨飞 许焱鑫 刘峻杉 |
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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 水利水电学院， 江苏 南京 210098;河海大学 水利水电学院， 江苏 南京 210098;河海大学 水利水电学院， 江苏 南京 210098;国电电力山东新能源开发有限公司， 山东 烟台 264003 |
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| 基金项目： | 国家自然科学基金重点项目（51139001）;国家自然科学基金青年科学基金项目（51209077）;江苏省普通高校研究生科研创新计划项目（SN：cxlx13 246） |
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| 摘 要： | 针对BP神经网络在大坝监测数据预测模型中后期预测精度不高的问题,基于小生境蚁群算法的智能搜索能力和强鲁棒性、BP神经网络对大量的输入-输出模式的非线性映射关系的学习存贮能力,将两种方法结合,用小生境蚁群算法优化BP神经网络的建模方法建立了水平位移观测数据的预测模型,并与ACA-BP神经网络和传统BP神经网络进行了对比分析。结果表明,本文方法可加快BP神经网络收敛速度、增强局部搜索能力,具有更高的预测精度。
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| 关 键 词： | 大坝安全监控 小生境技术 蚁群算法 BP神经网络 预测模型 |
Deformation Monitoring Model of Dam Based on NACA BP Neural Network |
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| WEI Wei,CHEN Chen,SHAO Chenfei,XU Yanxin and LIU Junshan.Deformation Monitoring Model of Dam Based on NACA BP Neural Network[J].International Journal Hydroelectric Energy,2014,32(8):85-87. |
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| Authors: | WEI Wei CHEN Chen SHAO Chenfei XU Yanxin and LIU Junshan |
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| Affiliation: | College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;;College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;;College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;;College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;;GD Power New Energy Development Co., Ltd., Yantai 264003, China |
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| Abstract: | Aiming at the low accuracy of BP neural network for dam monitoring prediction model in middle later period, based on the intelligent search ability and strong robustness of niche ant colony algorithm (NACA) and BP neural network which can learn and store a large number of input output nonlinear relationship, NACA is used to optimize BP neural network for establishing prediction model of horizontal displacement monitoring data. Compared with the traditional BP neural network prediction model, the results show that NACA BP neural network method can speed up the convergence rate of BP neural network, enhance local search ability and improve prediction accuracy. |
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| Keywords: | dam safety monitoring niche technology ant colony algorithm BP neural network prediction model |
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