【摘要】 用不同的L9(34)正交实验方案结果作为训练学习样本集 ,对BP神经网络预测应用过程的策略进行了探讨 ,结果表明 :完备的正交实验样本集是基本训练学习单元 ,在完备的正交实验样本集上添加或减少样本数量 ,所预测的结果是不可靠的 ;在同一类型、同一实验的条件下 ,完备的信息量大的正交实验样本集 ,能以很高的精度预测完备的信息量小的正交实验样本集 ;提出了一条新的实验设计思路———通过实验得出一个完备的正交实验样本集 ,通过计算机用BP神经网络就可以把与已知样本集有相同影响因素和水平的所有样本的值以相当高的精度预测出来 ,从而大大节省时间和劳力更多还原

【Abstract】 The strategy for forecasting the BP neural network was researched on the basis of the training studying samples that were obtained in the orthogonal test of L 9(3 4). The self\|contained orthogonal sample was the basic training and studying cell. When others samples were added into the self\|contained orthogonal samples or the self\|contained orthogonal samples were cut down, the forecasting results were completely irresponsible. On the same test condition and orthogonal test type, the self\|contained orthogonal sample with large information content could forecast that with small information content at high precision. A new test\|design approach was put forward. Namely, the self\|contained orthogonal sample was obtained through the orthogonal test, and then, the values of all other samples whose factors were the same as that of the self\|contained orthogonal sample could be forecast in the BP neural network and its precision was considerable high. Therefore, the time and labors were enormously saved. [更多还原