| 基于SVM的丙烯酰胺均相聚合预测 |
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| 引用本文： | 刘德玲. 基于SVM的丙烯酰胺均相聚合预测[J]. 计算机与数字工程, 2011, 39(4): 56-58 |
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| 作者姓名： | 刘德玲 |
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| 作者单位： | 广东食品药品职业学院,广州,510520 |
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| 摘 要： | 为了提高丙烯酰胺均相聚合预测的精度,建立了基于支持向量机的丙烯酰胺均相聚合预测模型,并采用此模型对实测数据进行了预测。与神经网络的预测结果相比,建立的新型聚合预测模型具有更好的预测精度。
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| 关 键 词： | 支持向量机 回归 丙烯酰胺 聚合预测 |
Mathematic Model for Predicting Viscosity of Amphoteric Polyacrylamide Based on SVM |
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| Liu Deling. Mathematic Model for Predicting Viscosity of Amphoteric Polyacrylamide Based on SVM[J]. Computer and Digital Engineering, 2011, 39(4): 56-58 |
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| Authors: | Liu Deling |
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| Affiliation: | Liu Deling(Guangdong Food and Drug Vocational College,Guangzhou 510520) |
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| Abstract: | Support vector machine is a novel statistical learning machine based on structural risk minimization principle instead of empirical risk minimization principle.in This paper presents a model of predicting viscosity of amphoteric polyacrylamide using Support Vector Regression（SVR）.The result show that compared with the neural networks statistical analysis method the proposed method can give more accurate learning precision and better generalization ability. |
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| Keywords: | support vector machine regression amphoteric polyacrylamider viscosity estimation |
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