Capacity_Optimization_for_Local_and_Cooperative_Sp

Root-MUSIC 算法进行了深入的研 究，表明波束域处理和Root-MUSIC 算法结合可以进一步提 高估计的分辨率和稳健性(Root-MUSIC algorithm for an in-depth study shows that domain-beam treatment and Root-MUSIC algorithm can further improve the estimated binding resolution and robustness)

核主元分析的程序，很好用，希望大家喜欢！！(KPCA procedure very well with the hope that you like! !)

说明：  基于matlab的标准粒子群优化算法,?趍atlab的标准粒子群优化算法,(Matlab based on the standard particle swarm optimization algorithm,? 趍atlab standard particle swarm optimization algorithm,)

particle swarm optimization for loss reduction

关于matlab优化在实际生活中的一些应用(Matlab optimization on some real life applications)

K近邻法的matlab程序，发现大家都在找它！(K-nearest neighbor method of Matlab procedures, I found that we all have to find it!)

RLS algorithm for matlab dsp

根据对真空蒸发镀膜的分析，在40×40的范围内分别蒸 发：1000、5000、10000、20000、30000个粒子。  利用2维数组记录每个点的粒子数。  初始粒的横纵坐标为0～40的均匀分布。  能量为（15，3）的正态分布。  在基体表面自由行走时损失1的能量。  若该点有粒子，即与其他粒子相遇时损失3的能量。  由低处向高处扩散，每扩散一层，损失4的能量，由高处 向低处扩散，损失3的能量。  当粒子的能量小于等于0时停留下来，该点的粒子数加1。  临界核的尺寸为10个粒子。  根据每个点粒子个数的不同，以不同的灰度显示这些点。 点越亮，则表示该点的膜越厚。  以图形方式显示粒子初始能量的正态分布情况(Based on the analysis of the vacuum evaporation coating, respectively, in the range of from 4040 evaporated: 1000,5000,10000,20000,30000 particles.  each point using 2-dimensional array to record the number of particles. The  initial grains transverse and longitudinal coordinates of a uniform distribution of 0 to 40. The  energy as a normal distribution (15,3).  1 energy losses in the substrate surface to walk.  point particles that 3 energy loss in the encounter with the other particles.  by the lower diffusion tops every diffusion layer, a loss of 4 energy diffusion from high to low, the energy loss of 3.  When the energy of the particles is less than or equal to 0:00 stay down, add 1 to the number of particles that point.  critical nucleus size of 10 particles.  these points, depending on the number of particles of each point is displayed in a different shade. Point, the brighter, the thicker the point of the fi)

说明：  四阶Runge-Kutta法解常微分方程组(Fourth Order Runge-Kutta Method for Solving Ordinary Differential Equations)

基于高维模型的径向基插值算法，用于求解高维模型系数，建立高维模型(Radial basis interpolation algorithm based on high dimensional model, used for solving high dimensional model coefficient, high dimensional model is established )