本文選題：重力壩變形預(yù)測(cè) + 多種群遺傳算法��； 參考：《蘭州理工大學(xué)學(xué)報(bào)》2016年05期

【摘要】：位移是重力壩變形監(jiān)測(cè)的重要物理量,對(duì)其進(jìn)行準(zhǔn)確預(yù)測(cè)是確保大壩安全運(yùn)行的前提.目前已經(jīng)有許多預(yù)測(cè)方法,但是大部分方法都存在易落入局部極小、收斂速度慢和收斂對(duì)初值敏感等問(wèn)題.為解決或減小這些問(wèn)題,提高預(yù)測(cè)精度,將多種群遺傳算法(MPGA)與反向傳播(BP)神經(jīng)網(wǎng)絡(luò)算法結(jié)合起來(lái),提出一種適用于重力壩變形預(yù)測(cè)的多種群遺傳神經(jīng)(MPGA-BP)網(wǎng)絡(luò)算法.實(shí)例計(jì)算證明,該算法能夠有效克服BP神經(jīng)網(wǎng)絡(luò)收斂速度慢、易出現(xiàn)局部極小值的缺點(diǎn)和遺傳算法的早熟收斂問(wèn)題,在進(jìn)行重力壩變形預(yù)測(cè)中具有更高的收斂性和精度.
[Abstract]:Displacement is an important physical quantity of gravity dam deformation monitoring, and accurate prediction is the prerequisite to ensure dam safe operation. At present, there are many prediction methods, but most of them are easy to fall into local minima, slow convergence speed and convergence sensitivity to initial values. In order to solve or reduce these problems and improve the prediction accuracy, a multi-population genetic neural network (MPGA-BPN) algorithm, which is suitable for gravity dam deformation prediction, is proposed by combining multi-population genetic algorithm (MPGA) with backpropagation (BP) neural network algorithm. Examples show that the algorithm can effectively overcome the shortcomings of slow convergence speed of BP neural network, local minimum and premature convergence of genetic algorithm. It has higher convergence and accuracy in gravity dam deformation prediction.
【作者單位】： 蘭州理工大學(xué)能源與動(dòng)力工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(51069004)
【分類(lèi)號(hào)】：TV642.3;TV698.11
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本文編號(hào)：1872710