共轭梯度法是最优化中最常用的方法之一，它具有算法简便、不需要矩阵存储等优点，十分适合于大规模优化问题。
Conjugate gradient method, which can be easily computed and requires no matrix storage, is one of the most popular and useful method for solving large scale optimization problems.
PR共轭梯度法是求解大型无约束优化问题的有效算法之一，但是算法的全局收敛性在理论上一直没有得到解决。
Pr conjugate gradient method is one of the efficient methods for solving large scale unconstrained optimization problems, however, its global convergence has not been solved for a long time.
共轭梯度法是求解最优化问题的一类有效算法。
Conjugate gradient methods are important iterative methods for solving optimization problems.
本文针对传统BP算法存在的两个常见问题进行了讨论，提出了基于步长优化和共轭梯度法的改进BP算法。
This paper discussed two basic problems of conventional BP algorithm. A improved BP algorithm based on step optimum and conjugate gradient was put forword in this paper.
本文介绍一种沿曲线方向线性搜索的算法，这种算法概括了梯度法、共轭梯度法以及它们的改进格式。
This paper introduces a curvilinear line searching technique, which possesses all the meris of the gradient method, the conjugate gradient method and some of their refined types.
将最速下降法与共轭梯度法有机结合起来，构造出一种混合优化算法，并证明其全局收敛性。
Based on the steepest descent method and the conjugate gradient method, a hybrid algorithm is proposed in this paper, and its global convergence is proved.
利用优化问题的非线性共轭梯度法与混沌优化方法相结合，提出了一种新的混合优化算法。
A new hybrid algorithm which combines the chaos optimization method and the nonlinear conjugate gradient method approach having an effective convergence property is proposed.
PCG算法是牛顿法和预优共轭梯度法结合起来解牛顿方程的一种非精确牛顿法。
Newton PCG method is an inexact Newton like method. It is an organic combination of Newton's method and preconditioned conjugate gradient method.
利用共轭梯度法的思想，建立相应的迭代算法。
A corresponding iterative method is presented by making use of conjugate gradient method.
通过离线的迭代算法生成高精度的样本点来训练神经网络，使用动量法、变学习率法和共轭梯度法提高BP网络的收敛速度。
Methods based on BP neural network and RBF neural network were studied to solve inverse kinematics. The training samples were obtained through off-line numerical method with high precision.
提出一种基于非线性共轭梯度法的唯相直接数据域最小二乘算法。
A phase-only direct data domain least square (D3LS) algorithm based on the nonlinear conjugate gradient method was proposed.
非线性优化技术、分枝定界算法和不完全乔莱斯基分解的预优共轭梯度法是该工作的三个主体部分。
Nonlinear programming techniques, branch and bound algorithms and incomplete Cholesky decomposition conjugate gradient method (ICCG) are the three main parts of our work.
就训练次数与精确度而言，它明显优于共轭梯度法及变学习率的BP算法，适用于系统辨识。
Concerned with the training process and accuracy, the LM algorithm is superior to conjugate gradient algorithm and a variable learning rate back propagation (BP) algorithm.
就训练次数与精确度而言，它明显优于共轭梯度法及变学习率的BP算法，适用于系统辨识。
Concerned with the training process and accuracy, the LM algorithm is superior to conjugate gradient algorithm and a variable learning rate back propagation (BP) algorithm.
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