基于人工神经网络的L9(34)正交实验预测特点研究

用五组L⁹(3⁴)正交实验结果作为训练学习样本集,通过人工神经网络对其预测特点进行了探讨.结果表明:完备的正交实验样本集是基本训练学习单元,不可分割,其预测结果与实测结果吻合很好.同一类型同一实验条件下,完备的信息量大的正交实验样本集通过人工神经网络能很好地预测其他完备的信息量小的正交实验样本集,这为正交设计提供了有意义的参考思路.

Forecasting Characters of L9(34) Orthogonal Test Based on the Artificial Neural Network

Five sets of results for different L_9(3~4) orthogonal tests were used as the training-study samples, the forecasting characters of L_9(3~4) orthogonal test were researched on the basis of the artificial neural network. The results showed that the self-contained orthogonal sample collection was the basic training-studying cell and was indivisible. Their forecasting results were tallied with the test results. When others samples were added into the self-contained orthogonal samples or the self-contained orthogonal samples were reduced, the forecasting results would be completely irresponsible. Under the same test conditions and with the same orthogonal test type, the self-contained orthogonal sample containing large information could forecast other self-contained orthogonal samples with small information. It provides a significant novel test-design approach for the orthogonal test.