發(fā)布時間：2018-06-22 19:31

  本文選題：過程神經(jīng)網(wǎng)絡(luò) + 極限學習　； 參考：《控制與決策》2017年04期

【摘要】：為了提高太陽黑子預(yù)測預(yù)報的精度,提出固定型極限學習過程神經(jīng)網(wǎng)絡(luò)(FELM-PNN)和增量型極限學習過程神經(jīng)網(wǎng)絡(luò)(IELM-PNN)兩種學習算法.FELM-PNN的隱層節(jié)點數(shù)目固定,使用SVD求解隱層輸出矩陣的Moore-Penrose廣義逆,通過最小二乘法計算隱層輸出權(quán)值;IELM-PNN逐次增加隱層節(jié)點,根據(jù)隱層輸出矩陣和網(wǎng)絡(luò)誤差計算增加節(jié)點的輸出權(quán)值.通過Henon時間序列預(yù)測驗證了兩種方法的有效性,并實際應(yīng)用于第24周太陽黑子平滑月均值的中長期預(yù)測預(yù)報中.實驗結(jié)果表明,兩種方法的預(yù)測精度均有一定程度的提高,IELM-PNN的訓練收斂性優(yōu)于FELM-PNN.
[Abstract]:In order to improve the accuracy of sunspot prediction, two learning algorithms, fixed limit learning process neural network (FELM-PNN) and incremental limit learning process neural network (IELM-PNN), are proposed. The number of hidden layer nodes of FELM-PNN is fixed. The Moore-Penrose generalized inverse of the hidden layer output matrix is solved by SVD, and the output weight of the hidden layer is calculated by the least square method. IELM-PNN increases the hidden layer node step by step, and the output weight value of the node is calculated according to the hidden layer output matrix and network error. The effectiveness of the two methods is verified by Henon time series prediction and is applied to the medium-long term prediction of sunspot smoothing monthly mean in the 24th cycle. The experimental results show that the prediction accuracy of the two methods is better than that of FELM-PNN to a certain extent, and the training convergence of IELM-PNN is better than that of FELM-PNN.
【作者單位】： 東北石油大學計算機與信息技術(shù)學院;山東科技大學信息科學與工程學院;
【基金】：國家自然科學基金項目(61170132) 黑龍江省自然科學基金項目(F2015021)
【分類號】：P182;TP183
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本文編號：2053985