LS_SVM和SVM在发酵过程建模中的比较

    Comparison Studies of LS_ SVM and SVM on Modeling for Fermentation Process

    • 摘要: 针对最小二乘支持向量机 (LS_SVM) 不需要指定逼近精度ε的特点, 比较了LS_ SVM与SVM两种方法利用生产数据为青霉素发酵过程建立的数学模型, 改进型GA分别为LS_ SVM和SVM选择参数值.实验证明:LS_ SVM建立的模型具有较高的拟合精度和泛化能力.如果ε过大时, SVM建立的模型的拟合精度和泛化能力不高;当ε过小时, 模型的拟合精度和泛化能力较高, 但耗时多.因此, LS_SVM更适合为发酵过程建模.

       

      Abstract: The SVM needs to use approximation accuracy ε, however the LS_ SVM doesn't need ε.According to this characteristics, the paper studied the fitting and generalization capabilities of models that LS_ SVM and SVM established for the penicillin fermentation process respectively.An improved GA selected the parameter values for LS_ SVM and SVM respectively.The experiment shows that the model based on LS-SVM possesses the strong capabilities of fitting and generalization.If ε is too large, the capabilities of fitting and generalization of model based on SVM are not high;if ε is too small, the capabilities of fitting and generalization are relatively high, but the modeling process demands long time.Therfore, the LS_ SVM is more suitable for modeling in fermentation processes.

       

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