On Using Quasi-Newton Algorithms of the Broyden Class for Model-to-Test Correlation
On Using Quasi-Newton Algorithms of the Broyden Class for Model-to-Test Correlation
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关于使用 Broyden 类拟牛顿算法进行模型与测试的关联
DOI：
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发表时间：
2014
期刊：
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通讯作者：
J. Klement
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文献类型：
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作者：
J. Klement
模型与试验结果的相关性是工程中的一项常见任务。通常遗传算法或自适应粒子群算法用于此任务。本文提出了另一种方法，使用两个拟牛顿算法的类定义Broyden。一项研究进行了热空间工业模型显示这种方法的性能。通过与其他研究的结果进行比较，表明这种方法减少了几个数量级的迭代次数。这将典型热模型相关性的计算时间从数周或数月减少到数小时或数天。
The correlation of a model with test results is a common task in engineering. Often genetic algorithms or adaptive particle swarm algorithms are used for this task. In this paper another approach is presented using two quasi Newton algorithms of the class defined by Broyden. A study is performed with thermal space industry models showing the performance of this approach. By comparing it to the results of other studies it is shown that this approach reduces the number of iterations by several orders of magnitude. This reduces the calculation time for a typical thermal model correlation from weeks or months to hours or days.