An Algorithm to Determine Sample Sizes for Optimization with Artificial Neural Networks
An Algorithm to Determine Sample Sizes for Optimization with Artificial Neural Networks
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DOI：
10.1002/aic.13871
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发表时间：
2013-03-01
期刊：
影响因子：
3.7
通讯作者：
Cremaschi, Selen
中科院分区：
文献类型：
--
作者：
Nuchitprasittichai, Aroonsri;Cremaschi, Selen
本文提出了一种算法，以确定适当的样本容量，构建准确的人工神经网络作为代理模型的优化问题。在该算法中，两种模型评估方法-交叉验证和/或引导-被用来估计各种网络的性能构造不同的样本量。最后以一个含胺水溶液的CO2捕集过程为例说明了该算法的应用。该算法的输出-使用适当的样本大小构建的网络-用于过程综合优化问题，以测试其准确性。结果表明，模型评估方法是成功的，在识别的基本模型的一般趋势和使用代理模型计算的最优解的目标函数值是在1%的实际值。(C)2012美国化学工程师学会AIChE J，59：805-812，2013
This article presents an algorithm developed to determine the appropriate sample size for constructing accurate artificial neural networks as surrogate models in optimization problems. In the algorithm, two model evaluation methods-cross-validation and/or bootstrapping-are used to estimate the performance of various networks constructed with different sample sizes. The optimization of a CO2 capture process with aqueous amines is used as the case study to illustrate the application of the algorithm. The output of the algorithm-the network constructed using the appropriate sample size-is used in a process synthesis optimization problem to test its accuracy. The results show that the model evaluation methods are successful in identifying the general trends of the underlying model and that objective function value of the optimum solution calculated using the surrogate model is within 1% of the actual value. (C) 2012 American Institute of Chemical Engineers AIChE J, 59: 805-812, 2013