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中文摘要：
变形是反映大坝动态演化的重要效应量。为了提升统计模型预测能力,借助极限学习机（ELM）处理非线性问题的优势,对大坝位移的统计模型残差进行数据挖掘。而极限学习机欠缺对混沌动力特性的考虑,为了解决这个问题,采用混沌理论对统计模型残差进行了混沌动力学特性分析,揭示其混沌特性,并据此重构相空间,从而为混沌优化极限学习机提供先验知识。基于统计模型,结合极限学习机和混沌理论的优点,建立统计模型与混沌优化ELM的组合模型。将该组合模型应用于工程实例,由多个定量评估指标对模型进行性能评价,结果表明,组合模型建模合理,预测精度高于统计模型、统计模型与混沌优化BP神经网络组成的组合模型,在大坝变形监测中具有一定的应用价值。
英文摘要：
Deformation is an important effect reflecting the dynamic evolution of a dam. In order to improve prediction precision of the statistic model, with the advantage of the extreme learning machine （ ELM） to deal with the nonlinear problems, data mining for dam displacements residuals of the statistic model is conducted. Because ELM is short of the chaotic dynamic characteristics, in order to solve this problem, the chaotic dynamic characteristics of the dam displacements residuals of the statistic model are analyzed by the chaos theory, the results reveal its chaotic characteristics, and then the phase space is reconstructed, thus it can provide priori knowledge for the chaos？optimized ELM. Based on the statistic model, combined with the advantages of ELM, a combined model combining a statistic model with the chaos？optimized extreme learning machine（ ELM） is developed. The combined model is applied to the case histories of practical engineering. The analysis results show that the combined model is reasonable, and the prediction precision is higher than the statistical model and the combined model combining the statistical model with the chaos？optimized BP neural network, which will be of application value to researchers in dam deformation monitoring.
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