算法优化了宏块帧间模式选择算法的流程。
The algorithm optimizes the flow of inter mode of macro-block decision algorithm.
这是一篇有关H . 264的算法优化的文章。
DOE的相位分布由数值迭代相位恢复算法优化得到。
The phase distribution of DOE is optimization result of iterative phase retrieval algorithm.
对同一组数据的求解，第三种遗传退火算法优化效率更高。
To solve the same group data, the third method (Genetic-Simulated Annealing) is more efficiency.
本文提出一种基于基因算法优化的自学习模糊控制器的设计。
This paper proposes a design of the self adaptive learning fuzzy controller based on Genetic Algorithms optimization.
为了保证游戏实时性，采取四种加速策略进行整体算法优化。
Four acceleration techniques are employed to achieve integral algorithm optimisation for ensuring real-time simulation effects.
在上述分析的基础上，通过模拟退火算法优化对编码进行组合优化。
On the base of analysis mentioned above, simulated Annealing Algorithm is used to optimize the codes.
在满足系统测量精度条件下，使用反向优选算法优化r BF网络结构。
In case of satisfying measure accuracy, backward selection method reduces the architecture of RBF networks.
神经网络建模和遗传算法优化是求解工程优化问题的一种行之有效的方法。
It is an efficient method to solve engineering optimization problems with neural networks and genetic algorithms.
运用空间映射算法优化天线阵的位置参量，可以大大提高运算速率，节省时间。
To use space mapping algorithm optimizing the position of the antenna, it can highly enhance the operation efficiency.
提出了一种利用遗传算法优化前向神经网络的结构和正则项系数的混合学习算法。
A hybrid learning approach is presented in which genetic algorithms are used to optimize both the network architecture and the regularization coefficient.
实验结果表明：算法优化后的神经网络分类器不但学习速度快，还能保证分类精度。
Experiment shows neural network classifier that is optimized by algorithm could not only have fast learning speed but also ensure accuracy of classification.
提出了一套嵌入式软件的优化流程，包括算法优化、实现优化和平台优化三个阶段。
This paper proposes a software optimization flow on embedded platform, which mainly includes algorithm optimization, implementation optimization and platform-based optimization.
针对模糊控制算法的不足，建立了双模控制器，用传统的PID算法优化模糊控制器。
For the shortage of fuzzy controller, the double model controller was adopted, optimizing fuzzy controller through traditional PID controller.
文中给出了两算法优化性能的分析，实验结果也显示这些算法的效果是明显而有效的。
For these two algorithms, we present formal analysis of optimality, and experimental results show that the algorithms are efficient.
本论文提出的多目标粒子群算法优化锅炉可调运行参数在锅炉燃烧优化领域还不多见。
It is an unusual case to use MOPSO to optimize operation parameters of boiler in the field of boiler combustion optimization.
本论文提出的多目标粒子群算法优化锅炉可调运行参数在锅炉燃烧优化领域还不多见。
It is an unusual case to use MOPSO to optimize operation parameters of boiler in the field of boiler combustion optimization.
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