linear optimization造句
例句与造句
- Design and implementation of linear optimization solving system
大规模线性优化求解系统的设计与实现 - Based on theory of convex cones , our extensions of robust linear optimization are done in three directions
摘要本文基于凸锥理论对鲁棒线性最优化作了若干拓展。 - The video - based 3d body tracking method under the non - linear optimization framework was proposed , which combined multiple cues and motion prior efficiently
其算法特点是，多种图像特征和运动知识有机地集成于一个基于非线性优化策略的跟踪框架中。 - Read sections 7 . 1 - 7 . 3 of chapter 7 . we will cover the basics of linear optimization , including formulations , key concepts , and graphical solution methods
阅读第七章7 . 1到7 . 3的部份。我们的课程将介绍线性最佳化的基础，包括方程式，重要概念以及图形解方法。 - A comprehensive set of lecture notes , from basic principles , such as linear optimization , to sophisticated real - world applications are available . also , there are problem sets in pdf format
本课程提供详尽的课堂讲稿，涵盖线性规划等基本原理以及复杂的实际问题应用。同时，本课还有一些问题集（ pdf格式） 。 - It's difficult to find linear optimization in a sentence. 用linear optimization造句挺难的
- Especially , a kind of non - linear optimization analytic hierarchy process ( ahp ) with experts reliability on the basis of the traditional ahp is proposed , and it is a new method for determining the evaluation indexes weights
特别地，文中在确定评价指标的权重时，在传统的层次分析法基础上进行改进提出了一种新方法带有专家可信度的非线性优化层次分析法。 - Traditional method can be classified two class : linear optimization technique and nonlinear optimization technique , linear optimization technique base on born approximation or rytov approximation is usually used to solve weak scattering problem
线性优化方法采用线性近似忽略了散射体内部的多次散射，可以有效的反演低对比度的问题，但对于高对比度问题的求解则有可能不收敛。 - By using rac ( radial alignment constraint ) of imaging process to decompose camera parameters and organizing the solving sequence of the parameters rationally , all parameters can be obtained through solving linear equations that avoid non - linear optimization
巧妙地利用成像过程中的径向约束（ rac ）分解摄像机参数，使得求解线性方程组即可得到全部的摄像机参数，避免非线性优化搜索。 - Compared with csm , two examples proved that ann could be trained successfully , even if the available data were insufficient and irregular , while csm showed the limit in selecting model type and non - linear optimization
两个实例的应用结果表明:人工神经网络通过神经原作用函数的简单复合就能逼近有限子集的任意非线性函数，而传统的统计方法则存在着如何选择模型形式及非线性优化问题，表现出明显的局限性，并且统计模型的更新工作相当繁重。 - With analogizing the evolution process of atomic transition from excited states to ground state , we proposed a novel non - linear optimization algorithm for geophysical inverse problem , called as simulated atomic transition algorithm ( sata )
在此基础上，模拟了物理学中原子从激发态向基态跃迁的物理过程，建立了一种与原子跃迁过程相对应的非线性随机跃迁数学模型和模型解跃迁搜索准则，导出了适用于一般地球物理资料的模拟原子跃迁的非线性反演算法。 - 2 . on the base of detailedly analysing the fourier neural networks , we find this neural networks have the characteristic which can transform the nonlinear mapping into linear mapping . so , we improve the original learning algorithm based on nonlinear optimization and propose a novel learning algorithm based on linear optimization ( this dissertation adopts the least squares method ) . the novel learning algorithm highly improve convergence speed and avoid local minima problem . because of adopting the least squares method , when the training output samples were affected by white noise , this algorithm have good denoising function
在详细分析已有的傅立叶神经网络的基础上，发现傅立叶神经网络具有将非线性映射转化成线性映射的特点，基于这个特点，对该神经网络原有的基于非线性优化的学习算法进行了改进，提出了基于线性优化方法(本文采用最小二乘法)的学习算法，大大提高了神经网络的收敛速度并避免了局部极小问题；由于采用了最小二乘方法，当用来训练傅立叶神经网络的训练输出样本受白噪声影响时，本学习算法具有良好的降低噪声影响的功能。 - We use rac ( radial alignment constraint ) of imaging process to decompose camera parameters . by organizing the solving sequence of the parameters rationally , we can obtain all parameters through solving systems of linear - 3 - abstract equations . accordingly we have changed the situation that ? he former camera calibration rac methods should depend on the non - linear optimization and has strict requirement to illumination , the situation that the calibrating distance is too short
算法考虑到摄像机模型中的一阶径向畸变，巧妙地利用成像过程中的径向约束（ rac ）分解摄像机参数，同时通过合理地组织参数的求解次序，使得经由求解线性方程组就可以得到全部的摄像机参数，从而改变了以往摄像机rac标定方法依赖于非线性优化，以及对光照条件要求严格和标定测定距离短的情况，使得rac方法较以往的算法更为精确、快速、简便，并且更加具有推广价值。