归纳学习优化仿真模型输出

Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM'99 (Cat. No.99EX296) Pub Date : 1999-07-01 DOI:10.1109/IPMM.1999.792488

R. Barton, H. Szczerbicka

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引用次数: 1

摘要

在本文中，我们提出了一种优化方法，“ML-Opt”，它通过分析搜索点之间的函数依赖关系来近似未知目标函数的结构。函数依赖关系由一种归纳学习算法确定，该算法生成一个分类器作为优化过程中的控制结构。给出了一个数值算例并进行了讨论。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Inductive learning for optimization of simulation model output

In this article we present the optimization approach, 'ML-Opt', which approximates the structure of an unknown goal function by analyzing functional dependency between search points. The functional dependency is determined by an inductive learning algorithm, which generates a classifier used as a control structure in the optimization process. A numerical example and discussions are presented.

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Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM'99 (Cat. No.99EX296)

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