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实验数据处理中曲线拟合方法探讨
Discussion on methods of curve fitting in experimental data processing
【摘要】 曲线拟合是实验数据处理的基本方法之一。将曲线拟合方法归结为有理论模型和无理论模型两类,据此,对曲线拟合的一般思路和重要方法进行了讨论。对两类方法进行了比较,并将它们联合用于对材料流变状态的速率-微分型本构模型的曲线拟合。
【Abstract】 Curve fitting is one of the basic methods in experimental data processing. In this paper, the methods of curve fitting are classified by existence of theoretic model. According to the above (classification), the paper discusses the general and important methods of curve fitting. Then it compares the two species of method and addresses a new method based on neural networks and least square. Finally, the new method is used for curve fitting of rheologic modelvelocity-differential constitutive relations.
【关键词】 数据处理;
曲线拟合;
最小二乘;
神经网络;
本构关系;
【Key words】 data processing; curve fitting; least square; neural networks; constitutive (relations);
【Key words】 data processing; curve fitting; least square; neural networks; constitutive (relations);
【基金】 国家973项目(G1999022511)
- 【文献出处】 成都理工大学学报(自然科学版) ,Journal of Chengdu University of Technology(Science & Technology Edition) , 编辑部邮箱 ,2004年01期
- 【分类号】TP391.41
- 【被引频次】187
- 【下载频次】4861