本文選題：參數(shù)估計(jì) + 連續(xù)Hammerstein模型�。� 參考：《長(zhǎng)沙理工大學(xué)》2011年碩士論文

【摘要】：課題以復(fù)雜機(jī)械系統(tǒng)動(dòng)力學(xué)建模與設(shè)計(jì)為工業(yè)應(yīng)用背景,在國(guó)家自然科學(xué)基金項(xiàng)目《連續(xù)非線性動(dòng)力學(xué)系統(tǒng)參數(shù)模型小波調(diào)制直接辨識(shí)》(項(xiàng)目編號(hào):50875028)資助下,擬定以帶測(cè)量噪聲情況下連續(xù)Hammerstein模型直接辨識(shí)為研究?jī)?nèi)容。主要進(jìn)行了以下幾個(gè)方面的研究: 1.建立了無測(cè)量噪聲干擾的連續(xù)Hammerstein模型的調(diào)制最小二乘直接辨識(shí)算法。介紹了非線性參數(shù)分離方法,用分離方法分解重整模型參數(shù)向量得到的乘積項(xiàng),最終得到連續(xù)Hammerstein模型的估計(jì)參數(shù)。 2.建立了有測(cè)量噪聲干擾情況的連續(xù)Hammerstein模型的調(diào)制最小二乘直接辨識(shí)算法。研究其噪聲的調(diào)制最小二乘特性,知其參數(shù)估計(jì)是有偏參數(shù)估計(jì),利用廣義噪聲模型處理調(diào)制噪聲,提出調(diào)制廣義最小二乘算法獲得了系統(tǒng)參數(shù)的無偏估計(jì)。 3.針對(duì)高噪聲情況下調(diào)制廣義最小二乘參數(shù)估計(jì)出現(xiàn)失真的情況,提出時(shí)窗小波降噪算法對(duì)帶高噪聲的數(shù)據(jù)進(jìn)行在線降噪,然后利用降噪數(shù)據(jù)估計(jì)模型參數(shù)。研究了時(shí)窗小波降噪在連續(xù)線性動(dòng)力學(xué)模型和連續(xù)Hammerstein模型中的應(yīng)用,由于降噪數(shù)據(jù)也帶有低噪聲,故分別提出時(shí)窗小波降噪調(diào)制輔助變量法和時(shí)窗小波降噪調(diào)制廣義最小二乘算法來獲得相應(yīng)模型參數(shù)的高精度的無偏估計(jì)。 4.對(duì)冷軋平整機(jī)HAGC壓力閉環(huán)系統(tǒng)建立連續(xù)線性動(dòng)力學(xué)參數(shù)模型和連續(xù)Hammerstein模型。將提出的算法用來辨識(shí)相應(yīng)的模型參數(shù),得到較好的辨識(shí)結(jié)果。應(yīng)用實(shí)例說明了算法的有效性和實(shí)用性。研究表明:本文研究的辨識(shí)算法對(duì)測(cè)量噪聲干擾下估計(jì)連續(xù)Hammerstein模型參數(shù)有較好的抗干擾能力,可適應(yīng)大型工業(yè)系統(tǒng)的模型辨識(shí)。
[Abstract]:Under the background of complex mechanical system dynamic modeling and design, supported by the National Natural Science Foundation of China, "Direct Identification of Parameter Model of continuous nonlinear dynamic system by Wavelet Modulation" (item No.: 50875028),Direct identification of continuous Hammerstein model with measurement noise is proposed.Mainly carried out the following aspects of research:1.A modulation least square direct identification algorithm for continuous Hammerstein model without measured noise interference is established.The nonlinear parameter separation method is introduced. The product term obtained from the parameter vector of the reforming model is decomposed by the separation method, and the estimated parameters of the continuous Hammerstein model are obtained.2.A modulation least square direct identification algorithm for continuous Hammerstein model with measured noise interference is established.The modulation least square characteristic of the noise is studied, and the parameter estimation is known as biased parameter estimation. The generalized noise model is used to deal with the modulation noise, and the modulation generalized least squares algorithm is proposed to obtain the unbiased estimation of the system parameters.3.Aiming at the distortion of modulated generalized least-squares parameter estimation under high noise, a time-window wavelet denoising algorithm is proposed to reduce the noise of data with high noise on line, and then the model parameters are estimated by denoising data.The application of time-window wavelet denoising in continuous linear dynamic model and continuous Hammerstein model is studied.Therefore, the time window wavelet denoising modulation auxiliary variable method and the time window wavelet denoising modulation generalized least square algorithm are proposed to obtain the high accuracy unbiased estimation of the corresponding model parameters.4.A continuous linear dynamic parameter model and a continuous Hammerstein model are established for the HAGC pressure closed loop system of cold rolling mill.The proposed algorithm is used to identify the corresponding model parameters, and better identification results are obtained.An example is given to illustrate the effectiveness and practicability of the algorithm.
【學(xué)位授予單位】：長(zhǎng)沙理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH113

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