| 1. | The actual rate for accurately recognizing 98 printed mathematical expressions is 0 . 867
该系统实际用于识别98个印刷体数学公式的正确识别率为0 . 867 。 |
| 2. | The experiment has proven that the techniques of handwriting feather extraction and decision can get the satisfying right identifying rate
本文运用的笔迹特征的分析方法和分类判别方法，经过大量的实验达到了满意的正确识别率。 |
| 3. | Simulation experiments have been made . and the results show that , the correct recognition rate with above 90 % is achi eved by using the algorithms given in this paper
对上述各算法，本文均进行了模拟试验，结果表明，综合运用本文中提出的图像处理与识别方法，正确识别率可达90以上。 |
| 4. | Experimental results demonstrate that kpca is not only good at dimensional reduction , but available to get better performance than conventional pca , of which the correct recognition rate is up to 92 . 5 %
实验结果表明，核主成分分析不仅实现了降维，而且能取得比传统主成分分析更好的识别性能，正确识别率为92 . 5 % 。 |
| 5. | Experimentation indicated that , to different goals , system had the correct discerning rate which is greater than 90 % , and it could give the result of discerning within 2 . 8ms , this can meet the real - time character request
实验表明系统对不同目标的正确识别率大于90 ，而且在2 . 8ms之内给出识别结果，可以满足实时性要求。 |
| 6. | ( 3 ) when the threshold of excess green , which is generated by red , green and blue without being normalized , is set at 30 , it can correctly segment plant from soil background . the correctness is over 99 %
( 3 )没有归一化的过绿特征受自然环境的影响较小，当取全局阈值30时，就能够有效识别植物与非植物背景，正确识别率在99以上。 |
| 7. | After alteration the structure became laconic , both training rate and correct recognition ratio were heightened . it became easier to extract rules from trained network basing on the characters of structure and knowledge distribution in the network
改进后网络在结构上比较简洁，训练速度和正确识别率都有了明显的提高，并且根据网络结构和知识分布的特点更容易实现规则抽取。 |
| 8. | A b - p neural network classifier is designed to the classification of three types of ship by using the feature vectors . the average correct recognition rate of three types ship reaches to 80 % . the result show that the above techniques based on hos are valid
将上述方法所提取的线谱和连续谱特征作为特征参量，采用b - p神经网络对三类船舶实际噪声样本进行识别分类，平均正确识别率达到80 。 |
| 9. | The correctness is over 99 % . ( 5 ) shape features studied were aspect , first invariant central moment , elongatedness , roundness , circularity and thickness . aspect and first invariant central moment are the most effective shape features for identifying monocotyledonous weed from dicotyledonous weed , and the correctness was 93 %
( 4 )利用修正的色度公式，由判别分析法确定色度阈值，对杂草图像进行阈值分割，能够有效地识别植物与非植物背景，正确识别率在99以上，但色度的计算量大于过绿特征的计算量，不利于杂草识别速度的提高。 |
| 10. | It is shown from our simulation that , when snr is no less than 15db , for awgn environment , the correct recognition rate is higher than 98 % and the average recognition time is about 3 . 2s ; for rican environment , the correct recognition rate is higher than 95 % and the average recognition time is about 3 . 26s ; for rican environment , the correct recognition rate is higher than 96 % and the recognition time is about 3 . 28s
仿真分析结果表明：在snr = 15db条件下，在awgn环境下，该设计的正确识别率不低于98 ，自动识别的平均大约时间为3 . 2秒；在rican环境下，该设计的正确识别率不低于95 ，自动识别的平均大约时间为3 . 26秒；在rummler环境下，该设计的正确识别率不低于96 ，自动识别的平均大约时间为3 . 28秒。 |
