共查询到19条相似文献,搜索用时 171 毫秒
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针对生物转化法制备丁二酸发酵过程机理复杂、高度非线性、生物参数难以实时在线测量等特点,采用基于结构风险最小化的支持向量机对发酵过程建模.与神经网络方法相比,所建模型的预测效果更好.同时采用粒子群算法对支持向量机模型中 相似文献
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发酵过程建模是研究微生物发酵的重要课题,基于模型可实现被测参量的软测量、系统的优化控制。鉴于引入混合核函数的最小二乘支持向量机在过程建模中具有优良表现,采用基于混合核函数的最小二乘支持向量机建模。但由于发酵过程周期较长,最小二乘支持向量机的全局模型预测精度难以保证,算法复杂度很高,因此提出一种分阶段建模方法。首先,选择表征阶段特性的辅助变量,利用模糊C均值聚类算法对样本数据聚类,将发酵过程分成不同的阶段,然后为各个阶段分别建立最优混合核最小二乘支持向量机局部模型,最后将局部模型合成构成过程的完整模型。将此方法应用于青霉素发酵过程和重组大肠杆菌发酵过程中,验证了该方法的有效性。 相似文献
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针对赖氨酸发酵过程非线性、大滞后、多变量动态耦合,关键生化参数难以实时在线测量等问题,提出一种改进的粒子群-支持向量机(PSO-SVM)逆发酵过程软测量建模方法。首先分析逆系统的存在性,并结合赖氨酸发酵过程,引入发酵特征信息和舍弃次要信息构造逆扩展模型;然后利用支持向量机离线辨识初始逆扩展模型,并根据系统输入与模型输出的偏差信号,采用粒子群算法对初始逆扩展模型进行在线校正;最后将校正后的逆扩展模型串联在原发酵过程之后构成复合伪线性系统,实现不直接可测关键生化参数的在线预测。以L-赖氨酸流加发酵过程为例,验证了所提算法能够对发酵过程关键生物量参数进行较准确的在线预测,较普通的SVM逆建模方法具有更高的预测精度。 相似文献
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针对青霉素发酵过程的参数检测存在不确定因素,提出一种基于混沌最小二乘支持向量机的青霉素浓度预测方案。采用混沌优化算法对最小二乘支持向量机参数进行寻优,建立了一种混沌最小二乘支持向量机模型。首先,利用该模型对两种常规非线性函数曲线进行了仿真回归,结果表明,算法具有良好的建模精度;其次,基于Pensim仿真平台,运用文中方法预测青霉素发酵过程的产物量,实验仿真表明混沌优化算法具有良好的全局优化性能,在参数选择中可以有效避免陷入局部最小值,基于混沌优化的最小二乘支持向量机具有较高的建模精度。 相似文献
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基于结构风险最小的最小二乘支持向量机(least squares support vector machine, LSSVM)为标准支持向量机(SVM)的约简;训练简易;性能良好。其模型精度受超参数影响;常规的网络搜索法很难搜得最佳超参数。在快速留一法的基础上;以全样本留一预测误差平方和最小化为目标;导出基于梯度的最优化算法;用以优选为LSSVM超参数;进而构建G-LSSVM模型。以柠檬酸发酵过程为算例对G-LSSVM进行检验;结果表明G-LSSVM的超参数选取耗时少;模型稳定性良好;且拟合和预报性能都优于标准SVM和神经网络。有望适用于机理不明、高度非线性、小样本的化工过程建模。 相似文献
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基于支持向量机的柠檬酸发酵过程统计建模 总被引:5,自引:1,他引:4
鉴于生物发酵过程的高度非线性,且样本采集困难,数据总量较少等,采用支持向量机(SVM)方法,为柠檬酸发酵过程建模,得到最终酸度与相关因素间的定量关系。通过优化建模参数,所建SVM模型具有较高的拟合能力,且预测误差小,稳健性好。实例表明,与人工神经元网络等方法相比较,SVM方法更为优越。 相似文献
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发酵过程生物量软测量技术的研究进展 总被引:4,自引:0,他引:4
生物量是发酵过程中的关键过程参数之一,它直接影响着发酵过程的优化和控制。综述了近年来发酵过程生物量软测量技术的研究现状,讨论了基于过程机理分析、回归分析、状态估计和神经网络等的软测量建模方法,对基于神经网络和改进的神经网络建模方法进行了分析。指出基于多尺度建立软测量混合模型,是实现发酵过程生物量在线测量的有效方法,并给出了建立混合模型需要解决的关键问题。 相似文献
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针对花生四烯酸(ARA)发酵过程复杂,机理模型表达不够准确以及单模型泛化能力弱的问题,提出采用基于仿射传播聚类的支持向量机(SVM)多模型建模算法进行该过程建模。该算法首先用仿射传播聚类(AP)算法对ARA样本数据进行聚类,再用SVM算法对各子类样本分别建立子模型。测试样本根据相似性的测度进行归类,并用所属子类的模型进行预测输出。ARA发酵过程的建模实验表明,与其他建模算法相比,基于仿射传播聚类的SVM多模型建模算法所建立的模型具有更高的回归精度和良好的泛化能力。 相似文献
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Natural gas load forecasting is a key process to the efficient operation of pipeline network. An accurate forecast is required to guarantee a balanced network operation and ensure safe gas supply at a minimum cost. Machine learning techniques have been increasingly applied to load forecasting. A novel regression technique based on the statistical learning theory, support vector machines (SVM), is investigated in this paper for natural gas short-term load forecasting. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization in conventional regression techniques. Using a data set with 2 years load values we developed prediction model using SVM to obtain 31 days load predictions. The results on city natural gas short-term load forecasting show that SVM provides better prediction accuracy than neural network. The software package natural gas pipeline networks simulation and load forecasting (NGPNSLF) based on support vector regression prediction 相似文献
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Natural gas load forecasting is a key process to the efficient operation of pipeline network. An accurate forecast is required to guarantee a balanced network operation and ensure safe gas supply at a minimum cost. Machine learning techniques have been increasingly applied to load forecasting. A novel regression technique based on the statistical learning theory, support vector machines (SVM), is investigated in this paper for natural gas shortterm load forecasting. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization in conventional regression techniques. Using a data set with 2 years load values we developed prediction model using SVM to obtain 31 days load predictions. The results on city natural gas short-term load forecasting show that SVM provides better prediction accuracy than neural network. The software package natural gas pipeline networks simulation and load forecasting (NGPNSLF) based on support vector regression prediction has been developed, which has also been applied in practice. 相似文献
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基于支持向量机(SVM)的软测量建模方法已经在工业过程控制领域得到广泛应用,然而传统支持向量机直接针对原始测量变量建立模型,未能充分挖掘数据的内在特征信息以提高预测精度。针对该问题,本文提出一种基于深度集成支持向量机(DESVM)的软测量建模方法。该方法首先利用深度置信网络(DBN)来对数据进行深层次的信息挖掘,提取出数据的内在特征,然后引入基于Bagging算法的集成学习策略,构建基于深度数据特征的集成支持向量机模型,以提升软测量预测模型的泛化能力。最后通过数值系统和真实工业数据对方法进行应用分析,结果表明本文提出的方法能够有效提升支持向量机软测量模型的预测精度,能够更好地预测过程质量指标的变化。 相似文献
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An optimal control strategy is proposed to improve the fermentation titer, which combines the support vector machine (SVM) with real code genetic algorithm (RGA). A prediction model is established with SVM for penicillin fermentation processes, and it is used in RGA for fitting function. A control pattern is proposed to overcome the coupling problem of fermentation parameters, which describes the overall production condition. Experimental results show that the optimal control strategy improves the penicillin titer of the fermentation process by 22.88%, compared with the routine operation. 相似文献
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《分离科学与技术》2012,47(18):2935-2951
ABSTRACTThis paper develops three models based on artificial neural network (ANN), support vector machine (SVM) and least square support vector machine (LSSVM) algorithm for phase behavior of thiophene/alkane/ionic liquid ternary system. The shuffled complex evolution (SCE) was employed to acquire the optimal magnitudes of hyper parameters (σ2 and γ) which are embedded parts of SVM and LSSVM models, and the trial and error was employed to obtain the optimal numbers of neuron and layers for ANN intelligent model. Gathering and using 618 LLE data, the comparison between the optimized version of applied intelligent models in giving the LLE was also made. The findings are indicative of a prefect agreement between the estimation from intelligent models and the experimental data. The finding also reveals that the performance of SVM in prediction of solubility is somewhat better than other intelligent models (i.e., ANN and SVM) as coefficient determination (R2) and root mean squared error (RMSE) are respectively 0.9961 and 0.0447 for test sets of data. This is likely due to the existence of structural risk minimization principle of SVM which is embodied in SVM algorithm and effectively minimizes upper bound of the generalization error, rather than minimizing the training error. 相似文献
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基于支持向量机的发酵过程生物量在线估计 总被引:5,自引:0,他引:5
Biomass is a key factor in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Therefore, the on-line estimation of biomass is indispensable. The soft-sensor based on support vector machine (SVM) for an on-line biomass estimation was analyzed in detail, and the improved SVM called the weighted least squares support vector machine was presented to follow the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments. 相似文献