CAJ | 학술논문

提出一种基于独立成分分析(ICA)的最小二乘支持向量机(LS-SVM),用于时间序列的多步超前独立预测.用ICA估计预测变量中的独立成分(IC),用不含噪声的IC重新构建时间序列.利用??-最近邻法(??-NN)减小训练集的规模,提出一种新的距离函数以降低LS-SVM训练过程的计算复杂度,并用约束条件对预测值进行后处理.使用基于ICA的LS-SVM、普通LS-SVM与反向传播神经网络(BP-ANN),对多个时间序列进行对比预测实验.实验结果表明,基于ICA的LS-SVM的预测性能优于普通LS-SVM和BP-ANN.
제출일충기우독립성분분석(ICA)적최소이승지지향량궤(LS-SVM),용우시간서렬적다보초전독립예측.용ICA고계예측변량중적독립성분(IC),용불함조성적IC중신구건시간서렬.이용??-최근린법(??-NN)감소훈련집적규모,제출일충신적거리함수이강저LS-SVM훈련과정적계산복잡도,병용약속조건대예측치진행후처리.사용기우ICA적LS-SVM、보통LS-SVM여반향전파신경망락(BP-ANN),대다개시간서렬진행대비예측실험.실험결과표명,기우ICA적LS-SVM적예측성능우우보통LS-SVM화BP-ANN.
@@@@A least square support vector machine (LS-SVM) based on the independent component analysis(ICA) is proposed to predict noisy non-stationary time series. ICA is used to estimate the independent components(IC) in the forecasting variables. After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the forecasting variables which contain less noise. A??-nearest neighbors(??-NN) approach is used to reduce the size of training dataset and a new distance function is defined. By selecting similar instances in the training dataset, the complexity of training a LS-SVM is reduced significantly. A boundary constraint component is developed to limit the predicted values to a reasonable range. The experimental results show that the proposed approach outperforms both traditional LS-SVM and BP-artificial neural network(BP-ANN) in the prediction performance of several time series.