对该参数体系进行噪声适应性测试的实验结果表明，无噪声情况下样本识别率为87.50%；
Next, noise compatibility test of this parameter system was carried on. The simulation result was: the sample recognition rate was 87.50% in non-noise condition;
基于粗糙集和神经网络的人脸识别方法是针对PC A方法中存在的高维数问题和它对未训练过的样本识别率低的缺点而提出的。
Face recognition based on rough set and neural network was proposed for the shortcoming of high dimension of PCA face recognition and low recognition rate for non-training samples.
在一个由1013个单词组成的样本中，这个系统的正确识别率达到98.41%，本文也会对这个系统可能潜在的应用加以讨论。
It was found that the system successfully identified 98.41% of words in a sample consisting of 1013 words. Potential applications of this system will also be discussed in this article.
应用推荐