| 1. | The system faces to application , and tries to overcome the noise affection and shorten the recognition time
课题的主要努力方向是针对实际应用进行系统设计、增强系统的抗噪声性能和减少识别时间。 |
| 2. | Compared with standard viterbi beam search algorithm , the adaptive algorithm that we present reduces recognition time by 35 . 56 %
与标准viterbibeam搜索算法相比，基于活动模型数变化的自适应viterbibeam搜索算法的搜索速度提高了35 . 56 。 |
| 3. | In asset stripping accounting , the focus is the handling of a department or a branch of corporation . accounting of transferring share focuses on recognition time and valuation basis
企业应根据资产重组的具体倩况，考虑经营风险对有关资产的影响，提供相关的以现实价值为基础的会计 |
| 4. | Part two , recognition of stock options . in this part , the recognition criteria , recognition period , initial recognition time of stock options are probed into
在简要介绍股票期权的几种确认观的基础上引出对股票期权的性质探讨，然后对股票期权的初始确认标准、确认期间及初始时点进行分析。 |
| 5. | On the other side , we use nearest neighbor approximation to calculate gussian mixture densities , which can reduce recognition time by 6 . 67 % compared with standard viterbi beam search algorithm
另一方面，使用高斯混合概率密度的最近邻快速估算方法，使标准viterbibeam搜索算法的搜索速度提高了6 . 67 。 |
| 6. | The simulation results show that the algorithm not only inherits the advantages of the original algorithm , but also both volume of the navigation star database and recognition time are equivalent to 1 / 4 that of the original algorithm
仿真结果表明本算法不但继承了原算法的优点，而且导航星库的容量和识别时间都是原算法的1 / 4 。 |
| 7. | When the algorithm is applied in the identification of industrial parts , comparison with the traditional bp neural network the recognition time will be shortened 2 . 8 second , and the recognition accuracy can reach more than 81 %
将上述算法应用于对工业零件的识别当中，相对于传统的神经网络可缩短识别时间2 . 8秒，而且识别正确率可达到81以上。 |
| 8. | This algorithm effectively overcomes the contradictions of traditional algorithm among recognition success rate , recognition time and storage capacity . and this algorithm has been improved notably in aspects of database capacity and real - time , comparing with traditional star pattern recognition algorithm
它有效地解决了传统算法在识别成功率、识别时间与存储量之间的矛盾，并且在数据库容量、实时性等方面较传统星图识别算法有显著改善。 |
| 9. | Further more , we improve the nearest neighbor approximation method by calculat e mixtures ordered by likelihood of being the best scoring mixture . the likelihood is calculating from previously processed data . this improved method can reduce recognition time by 15 . 56 % compared with standard viterbi beam search algorithm
本文对最近邻快速估算方法进行改进，在搜索过程中根据已处理过的数据统计出各个高斯混合分量产生最高对数概率的概率，并依此预测随后的计算中最有可能产生最高对数概率的高斯混合分量，优先计算更有可能产生最高对数概率的高斯混合分量，使标准viterbibeam搜索算法的搜索速度提高了15 . 56 。 |
| 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。 |
