摘 要
運行中的混凝土壩，本質上是一個復雜的非線性動力系統，目前的監測模型尚不能反映混凝土壩的非線性動力成分。混凝土壩變形監測資料蘊藏著大壩系統的本質特征，包括其混沌特性。通過對大壩變形監測資料中的混沌特性進行深入研究，建立相應的混沌分析和預測模型，對大壩安全監控具有十分重要的理論意義和應用價值。
本文以混凝土壩變形監測資料序列為研究對象，以混沌理論、相空間重構技術和人工神經網絡為研究手段，重點研究了混凝土壩變形中的混沌成分，建立了與統計模型互補的變形混沌模型。
本文的主要工作有：
（1）研究了混沌特征量的提取方法，相空間重構參數的確定方法，通過對幾種算法的比較分析，最終選取了適合數據長度較短且含有噪聲的時間序列混沌分析算法。
（2）分析了混沌時間序列相空間的預測方法，提出了在統計模型基礎上，分別結合自適應預測法和徑向基函數神經網絡的混沌預測模型。
（3）利用緊水灘大壩變形監測資料，通過建立統計模型提取殘差序列，然后對殘差序列進行混沌分析，重構殘差序列的相空間，應用兩類預測模型對其中的混沌成分進行預測，得到可以和統計模型相互補充的、有效的變形混沌預測模型。
目前，監測混沌模型的研究還剛剛起步，還有許多問題有待深入研究。如混沌模型的可預測尺度的提高問題，監測數據的降噪問題，與其他非線性理論聯合進行預測等。
關鍵詞：監測模型 混沌理論 相空間重構 變形預測 RBF神經網絡
Abstract
The operating concrete the current monitoring model can not reflect the nonlinear dynamic components. The dam is a complex nonlinear dynamic system in essence, deformation monitoring data contains the essential characters of the concrete dam, including its chaotic characteristics. An in-depth study of chaotic characteristics is made by establishing the corresponding chaotic analysis and prediction models, which has great theoretical significance and utility value for the dam safety monitoring.
On basis of the deformation monitoring data sequence of concrete dam, and taking the chaos theory, phase space reconstruction technology and artificial neural networks as research means, to focus on the study of its chaotic component, a deformation chaotic-prediction model is established which complements with the statistical model. The main work is:
(1) Study on the extraction method of chaotic characters and definite method of parameters for reconstructing phase space. By comparing several of the algorithms, it selected some logical algorithms for time series chaotic analysis, whose data is in shorter length and contains noise.
(2) Analyses the phase-space forecasting method of chaotic time series; put forward to two chaotic prediction models, which combines Volterra prediction method and radial basis function neural network respectively basing on statistical model.
(3) Using the monitoring data of JinShuitan dam, taking the residual sequence into Chaotic-analyzing and phase-space reconstruction after establishing the statistical model, and then predicting the chaotic components of which by utilizing the two model upper, in order to get the effective deformation chaos-prediction model.
At present, the study on chaotic monitoring model has just started that there are still many issues should to be studying in-depth, such as the predictable size of prediction model, noise reduction of observation data, the problem of combining other nonlinear theory into predicting, and so on.
Key words: Monitoring model，Chaos theory，Phase-space reconstruction，
Deformation Prediction，RBF neural network.
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