摘 要
運(yùn)行中的混凝土壩，本質(zhì)上是一個(gè)復(fù)雜的非線性動(dòng)力系統(tǒng)，目前的監(jiān)測模型尚不能反映混凝土壩的非線性動(dòng)力成分�；炷翂巫冃伪O(jiān)測資料蘊(yùn)藏著大壩系統(tǒng)的本質(zhì)特征，包括其混沌特性。通過對大壩變形監(jiān)測資料中的混沌特性進(jìn)行深入研究，建立相應(yīng)的混沌分析和預(yù)測模型，對大壩安全監(jiān)控具有十分重要的理論意義和應(yīng)用價(jià)值。
本文以混凝土壩變形監(jiān)測資料序列為研究對象，以混沌理論、相空間重構(gòu)技術(shù)和人工神經(jīng)網(wǎng)絡(luò)為研究手段，重點(diǎn)研究了混凝土壩變形中的混沌成分，建立了與統(tǒng)計(jì)模型互補(bǔ)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(gòu)參數(shù)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(shù)據(jù)長度較短且含有噪聲的時(shí)間序列混沌分析算法。
（2）分析了混沌時(shí)間序列相空間的預(yù)測方法，提出了在統(tǒng)計(jì)模型基礎(chǔ)上，分別結(jié)合自適應(yīng)預(yù)測法和徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)的混沌預(yù)測模型。
（3）利用緊水灘大壩變形監(jiān)測資料，通過建立統(tǒng)計(jì)模型提取殘差序列，然后對殘差序列進(jìn)行混沌分析，重構(gòu)殘差序列的相空間，應(yīng)用兩類預(yù)測模型對其中的混沌成分進(jìn)行預(yù)測，得到可以和統(tǒng)計(jì)模型相互補(bǔ)充的、有效的變形混沌預(yù)測模型。
目前，監(jiān)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(yù)測尺度的提高問題，監(jiān)測數(shù)據(jù)的降噪問題，與其他非線性理論聯(lián)合進(jìn)行預(yù)測等。
關(guān)鍵詞：監(jiān)測模型 混沌理論 相空間重構(gòu) 變形預(yù)測 RBF神經(jīng)網(wǎng)絡(luò)
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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