基于深度去噪核映射的长期预测模型
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摘要
针对最小二乘支持向量机核函数结构较浅对其长期预测模型精度的限制,采用深度学习中逐层特征提取的思想,提出基于深度去噪核映射的最小二乘支持向量机长期预测模型.该模型通过深度核网络的逐层变换,将样本数据映射到深度特征空间,从而有效提高其长期预测的精度.此外,为了提高模型对含高噪声数据的拟合性能,将去噪算法融入深度核网络的训练过程中,并通过反向传播算法对核网络参数进行整体微调.标准数据集及实际工业数据的仿真实验结果表明,所提方法能够有效提取数据中蕴含的特征信息,提高预测模型的精度.
In this study, employing the idea of layer by layer feature extraction in deep learning, a deep denoising kernel mapping-based least square support vector machine long-term prediction model is proposed. The proposed model can deal with the poor long-term prediciton ability problem of the least square support vector machine with shallow kernel structure. By transforming through the deep kernel network layer by layer, the sample data are mapped to the deep feature space to improve the prediction accuracy. In addtion, the denoising algorithm is incorporated into the training process of the deep kernel network to improve the fitting performance for the data with high level noises. Furthermore, the whole network is fine-tuned to further improve the modeling ability by using the back propagation algorithm. The results of the standard dataset and the actual industrial data simulation experiments show that the proposed method can extract the feature information contained in the data, and effectively improve the prediction accuracy.
In this study, employing the idea of layer by layer feature extraction in deep learning, a deep denoising kernel mapping-based least square support vector machine long-term prediction model is proposed. The proposed model can deal with the poor long-term prediciton ability problem of the least square support vector machine with shallow kernel structure. By transforming through the deep kernel network layer by layer, the sample data are mapped to the deep feature space to improve the prediction accuracy. In addtion, the denoising algorithm is incorporated into the training process of the deep kernel network to improve the fitting performance for the data with high level noises. Furthermore, the whole network is fine-tuned to further improve the modeling ability by using the back propagation algorithm. The results of the standard dataset and the actual industrial data simulation experiments show that the proposed method can extract the feature information contained in the data, and effectively improve the prediction accuracy.
引文
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[15] Xu J, Xiang L, Liu Q S, et al. Stacked sparse autoencoder(SSAE)for nuclei detection on breast cancer histopathology images[J]. IEEE Trans on Medical Imaging, 2016, 35(1):119-130.
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[17] Harrison D, Rubinfeld D L. Hedonic housing prices and the demand for clean air[J]. J of Environmental Economics and Management, 1978, 5(1):81-102.
[2] Giorgi M G D, Ficarella A, Tarantino M. Error analysis of short term wind power prediction models[J]. Applied Energy, 2011, 88(4):1298-1311.
[3] Chang W Y. Short-term wind power forecasting using the enhanced particle swarm optimization based hybrid method[J]. Energies, 2013, 6(9):4879-4896.
[4] Guan C, Luh P B, Michel L D, et al. Hybrid Kalman filters for very short-term load forecasting and prediction interval estimation[J]. IEEE Trans on Power Systems,2013, 28(4):3806-3817.
[5] Lauret P, Fock E, Randrianarivony R N, et al. Bayesian neural network approach to short time load forecasting[J].Energy Conversion and Management, 2008, 49(5):1156-1166.
[6] Guo W, Xu T, Lu Z L. An integrated chaotic time series prediction model based on efficient extreme learning machine and differential evolution[J]. Neural Computing&Applications, 2016, 27(4):883-898.
[7] Zhou J, Li X B, Shi X Z. Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines[J].Safety Science, 2012, 50(4):629-644.
[8] Suykens J A K, Gestel T V, Brabanter J D, et al. Least squares support vector machines[M]. Singapore:World Scientific Publishing Co, 2002:1-27.
[9] Kisi O, Parmar K S. Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution[J]. J of Hydrology, 2016, 534:104-112.
[10] Jin X, Sun Z, Wang H, et al. Application of improved support vector machine regression analysis for medium-and long-term vibration trend prediction[J]. J of Vibroengineering, 2013, 15(2):942-950.
[11] Xu Y, Wang J Y, Su H Y. Application of support vector machine based on genetic algorithm to mid-term and long-term run-off prediction[J]. J of Water Resources&Architectural Engineering, 2014, 12(5):42-45.
[12] Jebarani E S, Kumar S S, Jayakumar J. Torque modeling of switched reluctance motor using LSSVM-DE[J].Neurocomputing, 2016, 211:117-128.
[13] Mall R, Suykens J A K. Very sparse LSSVM reductions for large-scale data[J]. IEEE Trans on Neural Networks&Learning Systems, 2015, 26(5):1086-1097.
[14] Zhang X. White noise testing and model diagnostic checking for functional time series[J]. J of Econometrics,2016, 194(1):76-95.
[15] Xu J, Xiang L, Liu Q S, et al. Stacked sparse autoencoder(SSAE)for nuclei detection on breast cancer histopathology images[J]. IEEE Trans on Medical Imaging, 2016, 35(1):119-130.
[16] Zhang L, Wang F, Sun T, et al. A constrained optimization method based on BP neural network[J].Neural Computing&Applications, 2016, 7(11):1-9.
[17] Harrison D, Rubinfeld D L. Hedonic housing prices and the demand for clean air[J]. J of Environmental Economics and Management, 1978, 5(1):81-102.