本文選題：極大似然辨識(shí) + 信息濾波　； 參考：《江南大學(xué)》2017年博士論文

【摘要】：由于工業(yè)生產(chǎn)過程受到多種因素的干擾,使得有色噪聲干擾系統(tǒng)的辨識(shí)變得更加困難.論文通過極大化似然函數(shù),結(jié)合信息濾波技術(shù)研究一類有色噪聲線性系統(tǒng)和非線性系統(tǒng)的參數(shù)辨識(shí)問題,選題具有理論意義和應(yīng)用前景.取得了如下的成果.(1)針對(duì)標(biāo)量方程誤差A(yù)RMA系統(tǒng),提出了基于信息濾波的極大似然增廣梯度算法;為了加快梯度算法的收斂速度,引進(jìn)新息,提出了基于信息濾波的極大似然多新息增廣梯度算法.為了提高參數(shù)估計(jì)精度,推導(dǎo)了基于信息濾波的極大似然增廣最小二乘算法.進(jìn)一步,將提出的算法推廣到多變量方程誤差A(yù)RMA系統(tǒng)的參數(shù)估計(jì)中.(2)針對(duì)類多變量受控自回歸ARMA系統(tǒng),由于多變量系統(tǒng)的變量多、維數(shù)大,將系統(tǒng)分解成m(m是系統(tǒng)輸出的維數(shù))個(gè)子系統(tǒng),再利用信息濾波對(duì)每個(gè)子系統(tǒng)的輸入輸出數(shù)據(jù)進(jìn)行濾波,提出了子系統(tǒng)的信息濾波極大似然增廣梯度算法,并與極大似然廣義增廣梯度算法進(jìn)行比較,減少了有色噪聲對(duì)參數(shù)估計(jì)的影響,提高了參數(shù)辨識(shí)精度.(3)針對(duì)多輸入非線性Box-Jenkins系統(tǒng),為了解決其非線性及有色噪聲干擾的困難,利用分解技術(shù),研究了基于分解的極大似然廣義增廣最小二乘辨識(shí)算法;針對(duì)有色噪聲的干擾,選取適當(dāng)?shù)臑V波器,對(duì)系統(tǒng)的輸入輸出數(shù)據(jù)進(jìn)行預(yù)處理,再結(jié)合極大似然方法,提出了基于信息濾波技術(shù)的極大似然廣義增廣梯度辨識(shí)算法,減少了參數(shù)估計(jì)誤差.論文中,對(duì)所提出的辨識(shí)算法都結(jié)合了數(shù)值算例,進(jìn)行了仿真試驗(yàn),通過仿真例子驗(yàn)證了算法的有效性。
[Abstract]:Due to the interference of many factors in the industrial production process, it is more difficult to identify the colored noise jamming system. In this paper, the problem of parameter identification for a class of linear and nonlinear systems with colored noise is studied by means of maximum likelihood function and information filtering technique. The following results are obtained: (1) aiming at the scalar equation error ARMA system, a maximum likelihood augmented gradient algorithm based on information filtering is proposed, and in order to speed up the convergence of the gradient algorithm, new information is introduced. A maximum likelihood multi-innovation augmented gradient algorithm based on information filtering is proposed. In order to improve the accuracy of parameter estimation, a maximum likelihood augmented least square algorithm based on information filtering is derived. Furthermore, the proposed algorithm is extended to the parameter estimation of multivariable equation error ARMA system. (2) for multivariable controlled autoregressive ARMA system, because the multivariable system has many variables and large dimension, The system is decomposed into three subsystems, which are the dimension of the system output. Then the input and output data of each subsystem are filtered by information filtering, and the information filtering maximum likelihood augmentation gradient algorithm of the subsystem is proposed. Compared with the maximum likelihood generalized augmented gradient algorithm, the influence of colored noise on parameter estimation is reduced, and the accuracy of parameter identification is improved. For the multi-input nonlinear Box-Jenkins system, it is difficult to solve the nonlinear and colored noise disturbance. Using decomposition technology, the maximum likelihood generalized augmented least square identification algorithm based on decomposition is studied, and the appropriate filter is selected to pre-process the input and output data of the system, and then the maximum likelihood method is combined with the maximum likelihood method. A maximum likelihood generalized augmented gradient identification algorithm based on information filtering technique is proposed to reduce the error of parameter estimation. In this paper, the proposed identification algorithms are combined with numerical examples, and the effectiveness of the algorithm is verified by simulation examples.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：N945.14

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