Solving multi-response optimization problem using artificial neural network and PCR-VIKOR

M. Bashiri, A. Geranmayeh, M. Sherafati
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引用次数: 3

Abstract

In this paper a hybrid approach is introduced to solve multiple response problems. In the proposed method signal to noise (SN) ratio is computed and then SN ratios for unexperimented treatments are estimated using artificial neural network. The SN ratios are converted into a process performance index by applying process capability ratio and VIKOR method, so the treatments can be ranked and the best of them is selected. The performance of the proposed method is verified in a case study. Moreover a sensitivity analysis has been done by a VIKOR score estimator turned neural network. The results show efficiency of the proposed approach.
利用人工神经网络和PCR-VIKOR技术求解多响应优化问题
本文提出了一种求解多响应问题的混合方法。该方法首先计算信号的信噪比,然后利用人工神经网络估计未实验处理的信噪比。采用工艺能力比和VIKOR法将SN比转化为工艺性能指标,对各处理进行排序,选出最佳处理。通过实例验证了该方法的有效性。此外,还利用神经网络的VIKOR评分估计器进行了灵敏度分析。结果表明了该方法的有效性。
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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