An iterative search algorithm based on the Boltzmann machine.
波尔兹曼机的迭代搜索算法。
The problem of robustness in the estimation algorithm is solved by applying relax iterative search algorithm (RSA) to motion estimation of MPEG4 model based coding.
将自适应迭代松弛搜索算法RSA引入到MPEG4模型基编码的三维运动参数估计中 ，解决了估计算法的稳健性问题 。
The optimal iterative search process includes the selection of the parameter optimization algorithm, the gray interpolation calculation during the process of coordinate transformation.
其中优化迭代搜索过程又包括各参数优化算法的选取、坐标变换过程中灰度的插值计算等。
They are a kind of stochastic search iterative algorithm and no demand of optimization object.
它们均是一种随机搜索的迭代算法，对优化对象的性态无要求。
They are stochastic search iterative algorithms and no demand of optimization object.
它们均是随机搜索的迭代算法，对优化对象的初始状态无要求。
In addition, the branch-bound method is used in iterative process, these making it possible to reduce the search time in many situations.
此外在迭代过程中使用了分枝界限法，通常可显著减少迭代时间。
Based on analyzing user loads running characteristic and power system topology structure, this paper presents a new harmonic source separation method that is on-line search zero iterative method.
然后在分析用户负载运行特性的基础上，根据供电网络拓扑结构，提出一种新的谐波源分离方法—在线寻零迭代法。
Client resource index caching mechanism and the iterative query forward mechanism are adopted in the system design to enhance the search efficiency and success ratio.
系统采用客户资源索引缓存机制和“迭代深入”的查询转发机制，提高了搜索的效率和成功率。
By adopting the value iterative strategies of reinforcement learning, the algorithm can absorb the corresponding knowledge from its environment during its running and improve its search ability.
该算法采用强化学习中值迭代策略，在运行中能够从环境中获取相应知识，提高其搜索能力。
To shorten the convergence time, the optimal search problem of disparity map is converted to an iterative convergence process of bi-valued neural networks.
为加快收敛速度，该算法将视差图的最优搜索问题转换为二值神经网络的迭代收敛过程。
Based on this relation, boundary orthogonalization can be achieved by the search method of stepwise extension and the bisection algorithm with iterative methods in solving equations.
根据这种关系，可以用方程迭代求根方法中的增值寻根法和对分法，使网格边界正交化。
The running time of iterative-deepening-A~*(IDA~*) algorithm is analyzed with heuristic function of the problem space and the effect of the function is to reduce the actual search depth.
以问题空间上启发值的分布为启发函数的特征来分析迭代延伸A* （IDA~** ）的时间复杂度，使启发函数的作用相当于减小有效的搜索深度。
Then following the existing path-following method for solving BMI problem, an iterative LMI algorithm is proposed to locally search the desired output-feedback gain.
进一步，在问题有解时，通过极小化增益矩阵元素绝对值的和，给出了求解期望低成本输出反馈控制的算法。
Then following the existing path-following method for solving BMI problem, an iterative LMI algorithm is proposed to locally search the desired output-feedback gain.
进一步，在问题有解时，通过极小化增益矩阵元素绝对值的和，给出了求解期望低成本输出反馈控制的算法。
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