| 1. | Multi - stage ww algorithm of variable demand
需求变化的多阶段库存控制动态规划算法研究 |
| 2. | Solves the most short - path using the dynamic programming algorithm
利用动态规划算法求解最短路径 |
| 3. | In the tbd detection , dynamic programming ( dp ) technique was used
而tbd检测方法中,采用了动态规划算法。 |
| 4. | Most of sequence alignment software utilize dynamic programming ( dp ) or the hidden markov model ( hmm ) as the core algorithm for gap open and extension
序列比对分析是生物信息学研究中的一个基本内容。大多数序列比对软件以动态规划算法作为其空位插入的核心算法。 |
| 5. | It has been shown in numerical experiments that compared with the original dynamic programming method , the improved method can save the computational time and space with an optimal solution
数值实验表明，该算法可以缩简传统动态规划算法的计算时间和空间，同时得到解的最优值。 |
| 6. | This paper is to establish a system based on electronic map for motorcade - optimized dispatching , and dynamic programming algorithm is used to realize vehicle dispatching
本文的中心内容是：组建了一个基于电子地图大型车队优化调度及管理系统，并用动态规划算法实现了车辆的智能调度。 |
| 7. | An improved method is also presented according to the property of the new algorithm , with respact to the theoretical analysis , the applicable scope of the improved method is given and the future applicability is described
通过理论分析得到改进方案的适用范围，并描述了这一改进动态规划算法的应用前景。 |
| 8. | Dp technique is a high efficiency algorithm to solve combinatorial optimization problems by divided the multidimensional problem into multiple 1 - dimension problems . when this technique resorts to accumulate multiple frames , there are some disadvantages
动态规划算法是一种解决组合寻优问题的高效算法,通过将n维问题变换为n个一维优化问题,一个一个地求解的方法,很好的提高了效率。 |
| 9. | Basing on studying the principle of active contour model and some solutions to it , a new partial optimal dynamic programming contour detection algorithm is presented and the results comparable to the dynamic programming algorithm are given
在研究动态轮廓模型原理及其求解算法的基础上提出了一种新的部分最优化动态规划轮廓检测算法，保留了动态规划算法的性能和优点，但综合运算复杂度降低，运算速度大大提高。 |
| 10. | It can take advantage of the advancement of hmm and gmm , utilize dynamic programming technique to realize the nonlinear time alignment between speech feature vectors and markov state sequences , use expectation - maximum algorithm to re - estimate the gmm parameters and finally employ levenshtein distance to calculate the word error rate between the recognized and expected results
它将隐markov模型和gaussian混合密度分布紧密联系,结合动态规划算法对时间序列和markov状态链进行非线性时间对齐,并运用em算法对gaussian混合模型的参数进行重新估计,识别出来的结果与期望结果采用levenshtein距离进行比较并得出其字误差率。 |
