【摘要】：針對(duì)具有噪聲干擾的非線性時(shí)變系統(tǒng)建模時(shí)存在的困難,建立了一種具有最優(yōu)結(jié)構(gòu)和最佳泛化能力的多維泰勒網(wǎng)模型,以實(shí)現(xiàn)對(duì)該系統(tǒng)的辨識(shí).首先,為了能夠快速反映系統(tǒng)輸入/輸出的變化,以多維泰勒網(wǎng)的連接權(quán)系數(shù)作為時(shí)變參數(shù),并由帶可變遺忘因子的遞推最小二乘算法對(duì)其進(jìn)行訓(xùn)練,進(jìn)而討論了辨識(shí)方案的穩(wěn)定性.然后,為了避免維數(shù)災(zāi)難并滿(mǎn)足實(shí)時(shí)性要求,給出了選擇多維泰勒網(wǎng)有效回歸項(xiàng)的改進(jìn)權(quán)衰減法,以使多維泰勒網(wǎng)同時(shí)具有最小結(jié)構(gòu)和最佳的泛化能力.最后,通過(guò)算例說(shuō)明基于最優(yōu)結(jié)構(gòu)的多維泰勒網(wǎng)在含噪聲非線性時(shí)變系統(tǒng)辨識(shí)問(wèn)題中應(yīng)用的方法,算例結(jié)果驗(yàn)證了該方法的有效性.
[Abstract]:Aiming at the difficulties in modeling nonlinear time-varying systems with noise disturbance, a multi-dimensional Taylor net model with optimal structure and optimal generalization ability is established to identify the system. Firstly, in order to reflect the change of input / output of the system quickly, the connection weight coefficient of multi-dimension Taylor net is taken as the time-varying parameter, and it is trained by the recursive least squares algorithm with variable forgetting factor. Furthermore, the stability of the identification scheme is discussed. Then, in order to avoid dimensionality disaster and meet the real-time requirements, an improved weight attenuation method for selecting effective regression terms of multi-dimensional Taylor nets is presented, so that the multi-dimensional Taylor nets have the minimum structure and the best generalization ability at the same time. Finally, an example is given to illustrate the application of multi-dimensional Taylor nets based on optimal structure to the identification of nonlinear time-varying systems with noise. The effectiveness of the proposed method is verified by an example.
【作者單位】： 東南大學(xué)自動(dòng)化學(xué)院;河南工學(xué)院計(jì)算機(jī)科學(xué)與技術(shù)系;東南大學(xué)復(fù)雜工程系統(tǒng)測(cè)量與控制教育部重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61673112,60934008) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專(zhuān)項(xiàng)資金資助項(xiàng)目(2242017K10003,2242014K10031) 江蘇高校優(yōu)勢(shì)學(xué)科建設(shè)工程資助項(xiàng)目
【分類(lèi)號(hào)】：TB53

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