Data approximation using Lotka-Volterra models and a software minimization function

IF 0.3 Q4 MATHEMATICS, APPLIED Journal of Applied Mathematics Statistics and Informatics Pub Date : 2019-12-01 DOI:10.2478/jamsi-2019-0005
Michal Feckan, J. Pacuta
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引用次数: 1

Abstract

Abstract In recent years, a lot of effort has been put into finding suitable mathematical models that fit historical data set. Such models often include coefficients and the accuracy of data approximation depends on them. So the goal is to choose the unknown coefficients to achieve the best possible approximation of data by the corresponding solution of the model. One of the standard methods for coefficient estimation is the least square method. This can provide us data approximation but it can also serve as a starting method for further minimizations such as Matlab function fminsearch.
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数据近似使用Lotka-Volterra模型和软件最小化函数
摘要近年来,人们一直在努力寻找适合历史数据集的数学模型。这种模型通常包括系数,数据近似的准确性取决于它们。因此,目标是选择未知系数,通过相应的模型解来实现数据的最佳逼近。系数估计的标准方法之一是最小二乘法。这可以为我们提供数据近似,但也可以作为进一步最小化的起始方法,如Matlab函数fminsearch。
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自引率
0.00%
发文量
8
审稿时长
20 weeks
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