搜索资源列表
bound-constrained_SVM
- bound-constrained SVM
simulation_hua_square_obstacle_2
- 轮式移动机器人的避障,可以实现机器人在约束环境中的期望运动-Wheeled mobile robot obstacle avoidance can be achieved in the constrained environment of the robot s expectations Movement
F-R
- 这个是优化方法中的一种方法,,是一种用于带约束条件的优化。-This is the optimization method in a way, is a condition for constrained optimization.
ga_matlab
- matlab实现遗传算法程序,用于求解约束的优化问题-matlab genetic algorithm to achieve the procedure for solving constrained optimization problem
conjugate_grad_2d
- 此算法采用matlab7.0环境下编写的代码,用于求解线性规划问题中非约束条件下的最优解问题。-This algorithm uses matlab7.0 environment code written for solving linear programming problems under the conditions of the Central African constrained optimal solution problem.
NIP
- matlab 7.0 以上版本提供了强大的优化工具箱,但在整数规划方面,只提供了bintprog()这个m文件以求解0-1整数规划,而对于一般的整数规划模型没有具体的算法提供。我们一般情况只是用最简单的分值定界思想编写matlab程序求解整数规划问题,但效率低下,如何利用求解整数规划的先进算法编写matlab程序提上日程,香港大学的李端和复旦大学编写的《Nonlinear Integer Programming》(非线性整数规划)为编写解决整数规划问题提供强大有效的算法,其中算法针对具体问题包括
outclpso
- 用于分析带约束问题的输出结果,加上约束的粒子群算法功能有很大改善-Used to analyze the problem with constraint output, coupled with constrained particle swarm optimization function has greatly improved
7941925pos
- 粒子群的优化算法,不仅可以方便地解决无约束优化问题,也可以方便的解决有约束的非线性优化问题。-Particle Swarm Optimization algorithm, not only can easily solve the unconstrained optimization problem can also be convenient to solve constrained nonlinear optimization problem.
fmincon
- 通过使用matlab优化工具箱中的函数fmincon解决有约束的非线性优化问题,有详细的代码过程。-By using the matlab optimization toolbox function fmincon to solve constrained nonlinear optimization problem, has detailed the process code.
Imagefeaturelocalization
- 介绍一种图像特征点定位方法,用以解决现有技术中存在的图像特征点定位效率低、应用受限的问题。-Introduce an image feature point positioning methods, the methods used to tackle existing technologies that exist in image feature point positioning low efficiency, the application of constrained problem.
new_sqp
- 通过对经典的lemke互补转轴算法求解含有等式约束的凸二次规划问题的分析,发现所得到的线性互补问题(lcp)可能是退化的.由lemke算法求解(lcp)问题的迭代过程,通过六个命题说明了含有等式约束的凸二次规划问题对应的(lcp)问题退化的原因,并对经典的lemke算法的迭代过程进行修正,提出了一种改进的lemke算法,这种算法能有效地搜索到含等式约束凸二次规划问题的最优解.-Through the classic Lemke complementarity algorithm shaft co
Beamformingformovingsourcespeechenhancement
- 争对移动声源采用波束形成进行语音增强,提出一种约束子带波束形成算法。其波束形成器基于一个软约束,其目的是要使波束指向特定的区域即声源方向。而其核心在于首先要进行声源定位,获得尽量准确的方位信息,然后构造软约束条件,用于波束形成。且在此过程中不断跟踪声源的移动情况。在构造的约束条件中,需要知道声源的二维信息,即与麦克风阵列的距离和方向角,“软”体现在对距离和方向角的确定都是在一定范围内的,有待进一步更正。-Between the mobile sound source using beamform
MAPsegm
- A Spatially-Constrained Mixture Model for Image Segmentation, by K. Blekas, A. Likas, N. Galatsanos and I. Lagaris-A Spatially-Constrained Mixture Model for Image Segmentation, by K. Blekas, A. Likas, N. Galatsanos and I. Lagaris
matlab_trm
- MATLAB 有约束信赖域算法,以四元多项式为算例 适用于学习最优化算法的数学专业学生以及其他数值分析课程的同学, 程序清晰,对MATLAB的学习也有很大的帮助,同时程序还有一些不足,读者自己须根据实际问题更正。-MATLAB constrained trust region algorithm to quaternion polynomial example for the application of optimization algorithms in the learning o
beamformer
- Beamforming thesis describing Study of a various Beamforming Techniques And Implementation of the Constrained Least Mean Squares (LMS) algorithm for Beamforming
c
- 罚函数方法是求解约束(极小)优化问题的一类较好的算法。其基本思想:根据约束的特点构造某种惩罚函数,并把惩罚函数添加到目标函数上去,从而得到一个增广目标函数,使约束优化问题的求解转化为一系列无约束极小优化问题的求解。-Penalty function method for solving constrained (minimum) optimization problem of a class of better algorithms. The basic idea: In accordance
a
- 约束优化方法—惩罚函数法的c++源程序,可用于三维变量。-Constrained optimization methods- penalty function method of c++ source code, can be used for three-dimensional variable.
p1
- 巨大的数据量使显示速度成为制约普及和网络化的重要因素,文章结合4(5’公司的607(84空间数据引擎,提出了一种利用分级技术提高矢量图显示速度的方案,并讨论了几种插值算法在这一应用中的优缺点。- W ith the ant colony algorithm for solving the traveling salesman problem (TSP) as a prototype, a simpli- fied algorithm was developedwhich considered
picture_processing
- 图像处理包括(1) 显示图片 (2) 正交变换--包括傅立叶变换、沃尔什变换、离散余弦变换 (3) 图象增强--包括直方图显示、图象均衡化、图象规定化、图象平滑、锐化、中值滤波、低通滤波器、伪彩色编码 (4) 图象复原--非约束复原(逆滤波)、约束复原(维纳滤波) -Image processing, including (1) Show pictures (2) orthogonal transformation- including the Fourier transform
01020231-from-ntmgz-hxy
- In the next generation of wireless communication systems, there will be a need for the rapid deployment of independent mobile users. Significant examples include establishing survivable, efficient, dynamic communication for emergency operations,