Convergence of the algorithm is proved.
并证明了算法收敛性。
The convergence of the algorithm is proved.
算法的收敛性证明。
The proof of convergence of the algorithm is given.
算法的收敛性证明。
The convergence of the algorithm is theoretically analyzed.
文章从理论上分析了算法的收敛性。
The convergence of the algorithm is given in the sense of energy norm.
在能量范数意义下给出了算法的收敛性。
The efficient preconditioned methods will accelerate the convergence of the algorithm.
同时，正确有效的预处理方法能加快迭代收敛。
The performance and convergence of the algorithm are analyzed and compared in the paper.
最后，本文在上述研究的基础上，编程实现了布局算法程序。
The strong convergence of the algorithm under a general choice for radius of trust region is proved.
在一个很广泛的信赖域半径选择规则下，证明了算法的强收敛性。
The algorithm is initialized by a statistical histogram based on FCM algorithm, which can speed up the convergence of the algorithm.
算法中使用基于统计直方图的快速FCM算法进行初始化，收敛速度大大提高。
In this paper, the influence about system initial shift and system parameter disturbance on convergence of the algorithm is studied.
本文研究系统状态初值漂移和系统参数扰动对迭代学习控制算法收敛性的影响。
Also the proof of global convergence of the algorithm is presented, which is of value for discussions of convergence of more general algorithms.
还证明了该算法的总体收敛性，其证明方法对一般迭代算法的收敛性讨论具有参考价值。
In this paper, we propose a new descent direction. Under the pseudomonotone of the underlying function, we prove the global convergence of the algorithm.
提出一个新的下降方向，在函数伪单调的条件下证明了算法的全局收敛性。
We present a new branchandbound algorithm for solving quadratic programming problem with quadratic constraints, and analyze the convergence of the algorithm.
提出了一种解带有二次约束二次规划问题的新的分枝定界算法对该算法进行了收敛性分析。
This paper presents a new descend algorithm for nonlinear complementarity problems. The global convergence of the algorithm is proved under milder conditions.
针对非线性互补问题，提出了与其等价的非光滑方程的一个下降算法，并在一定条件下证明了该算法的全局收敛性。
A smoothing approximation algorithm for nonlinear complementarity problems was introduced and the global convergence of the algorithm was proved under milder conditions.
提出了求解非线性互补问题的一个光滑逼近算法，在一定条件下证明了该算法的全局收敛性。
Based on the model, the convergence of the algorithm is analyzed and the global convergence of the algorithm is proved when leaving the divert probability out of account.
在不考虑状态转移概率的情况下证明了思维进化算法能够收敛到全局最优解。
In this paper, a new branch and its bound algorithm for solving integer separable concave programming problems is proposed, and the convergence of the algorithm is proved.
提出了一种新的解整数可分离凹规划问题的分支定界算法，并证明了其收敛性。
During the alternate iteration, an acceleration method called as vector extrapolation was applied to the alternate iteration steps owing to slow convergence of the algorithm.
在交替迭代过程中，考虑到算法收敛较慢，一种称为向量外推的加速方法被采用。
In this paper, a lower approximating algorithm of large-scale concave quadratic programming in unbounded domain is constructed. The convergence of the algorithm is discussed.
本文给出了无界域上大规模凹二次规划的一种下逼近算法，并证明了算法的收敛性。
Simulated annealing mechanism is introduced to do local-search for the best chromosome in every generation of the evolution process. This improves the convergence of the algorithm.
算法引入模拟退火机制，在遗传进化过程中的每一代，对最优个体进行邻域局部寻优，利用模拟退火进一步改善算法的收敛性能。
Aiming at the shortcomings of trust region method, we proposed an algorithm using negative curvature direction as its searching direction. The convergence of the algorithm was given.
针对模型信赖域方法中搜索方向存在的不足，提出了按负曲率方向进行搜索的模型信赖域算法，并证明了算法的收敛性。
Then the mechanism of Simulated Annealing is import in the algorithm above to decrease the execution time and quickens the velocity of convergence.
然后，为了加快遗传算法的收敛速度减少算法执行时间引入模拟退火机制对上述算法进行优化。
Based on building up a model of echo canceller, the convergence of gradient-type stochastic adjustment algorithm of an adaptive filter under the mean-squared error criterion is discussed.
在建立回波抵消器模型的基础上，按最小均方误差准则，导出了自适应滤波器抽头的统计梯度算法和抽头调节的收敛公式。
Then immune evolutionary algorithm is used to train the RBF network, which reduces the searching space of canonical evolutionary algorithm and improves the convergence speed.
采用免疫进化算法训练r BF网络，进一步缩小了标准进化算法搜索空间的范围，提高了算法的收敛速度。
According to the analysis of simulation results compared with BP algorithm, this algorithm has the advantage of the fine stability, fast convergence speed and high precision.
通过与BP算法的仿真结果比较分析，发现该算法具有稳定性好，收敛速度快，预测精度高的特点。
The simulation and motor control show that the new algorithm has fast learning rate, good convergence properties and can overcome the defects of traditional PID algorithm.
仿真实验及在伺服电机转速控制中的应用表明，该算法具有较快的学习速度及良好的收敛性能，并有效地克服了传统PID算法的缺陷。
The simulation and motor control show that the new algorithm has fast learning rate, good convergence properties and can overcome the defects of traditional PID algorithm.
仿真实验及在伺服电机转速控制中的应用表明，该算法具有较快的学习速度及良好的收敛性能，并有效地克服了传统PID算法的缺陷。
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