發(fā)布時間：2018-06-03 15:20

  本文選題：支持向量回歸 + 概率分布　； 參考：《控制理論與應(yīng)用》2017年05期

【摘要】：傳統(tǒng)支持向量回歸是單純基于樣本數(shù)據(jù)的輸入輸出值建模,僅使用樣本數(shù)據(jù)信息,未充分利用其他已知信息,模型泛化能力不強.為了進一步提高其性能,提出一種融合概率分布和單調(diào)性先驗知識的支持向量回歸算法.首先將對偶二次規(guī)劃問題簡化為線性規(guī)劃問題,在求解時,加入與拉格朗日乘子相關(guān)的單調(diào)性約束條件;通過粒子群算法優(yōu)化懲罰參數(shù)和核參數(shù),優(yōu)化目標包括四階矩估計表示的輸出樣本概率分布特性.實驗結(jié)果表明,融合這兩部分信息的模型,能使預(yù)測值較好地滿足訓(xùn)練樣本隱含的概率分布特性及已知的單調(diào)性,既提高了預(yù)測精度,又增加了模型的可解釋性.
[Abstract]:The traditional support vector regression is an input and output Zhi Jianmo only based on sample data, only using sample data information and not fully utilizing other known information, the model generalization ability is not strong. In order to further improve its performance, a support vector regression algorithm which combines probability distribution and monotonicity prior knowledge is proposed. First, the dual two times are combined. The programming problem is simplified as a linear programming problem. In the solution, the monotonicity constraint conditions related to the Lagrange multiplier are added. The particle swarm optimization is used to optimize the penalty parameters and the kernel parameters. The optimization target includes the probability distribution characteristics of the output samples represented by the four order moment estimation. The experimental results show that the model which combines the two parts of information can make the preview of the model. The measured values satisfactorily satisfy the implicit probability distribution characteristics and known monotonicity of training samples, which not only improve the prediction accuracy, but also increase the interpretability of the models.
【作者單位】： 華東理工大學(xué)化工過程先進控制和優(yōu)化技術(shù)教育部重點實驗室;
【基金】：國家自然科學(xué)基金項目(21176073) 國家“973”計劃(2013CB733605)資助~~
【分類號】：TP18

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