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geng13
- 用matlab仿真的xc = [1 1 1 1 1 1 1 1 1 1] 的fft时序幅频图形-simulation using Matlab xc = [1 1 1 1 1 1 1 1 1 1] fft timing frequency graphics
circle_fit
- 圆拟合，通过已知的多组数据，拟合出最小误差的圆-Given a set of measured x,y pairs that a re supposed to reside on a circle, but with some added noise. A circle to these points, i.e. find xc,yc,R, such that (x-xc)^2+(y-yc)^2=R^2
xc
- 基于强化学习的机器人寻路，以方格地图为环境-reinforcement learning apply in the robatics
dengyi
- CONSX—这是一个主要子程序，调用其它于程序及输出中间结果 * !* CHECK—检查所有的点是否满足约束条件,对违背约束的点进行校正 * !* CENTR—计算中心点 * !* FUNC —目标函数，由用户提供 * - PROGRAM COMPLEX PARAMETER(N=3,M=4,K=6) DIMENSION X(K,M),R(K,N),F(K),G(M),H(M),XC(N) INTEGER GAMMA OPEN(4,FILE= COMPDA
xc164
- xc164中文手册，详细的介绍了xc—164的各个模块的功能- xc164 Chinese handbook Detailed introduction xc-164 each module function
sdpfcbt
- 简单的脚本来读取视频文件，并跟踪单个二维红色标记使用的色调和饱和度值。最大的红色斑点检测，平均统筹此Blob位置跟踪。这段代码没有任何跟踪内置的：它把每一帧独立。 这是假设是一个常数的视频帧速率视频。由此产生的协调时间序列存储在变量：吨，结晶度和YC。-Simple scr ipt to read a video file and track a single red marker in 2D using the hue and saturation values. The large
circle_fit
- Revival of a 14 years old code (written in 1991 for MATLAB 2.x). Given a set of measured x,y pairs that a re supposed to reside on a circle, but with some added noise. A circle to these points, i.e. find xc,yc,R, such that (x-xc)^2+(y-yc)^2=R^2
evolve
- matlab pso-xc evolve.m matlab pso-xc evolve.m
evolve2
- matlab pso-xc evolve2.m matlab pso-xc evolve.m
Radial-basis-function
- 径向基函数是一个取值仅仅依赖于离原点距离的实值函数，也就是Φ（x）=Φ(‖x‖),或者还可以是到任意一点c的距离，c点称为中心点，也就是 Φ（x，c）=Φ(‖x-c‖)。任意一个满足Φ（x）=Φ(‖x‖)特性的函数Φ都叫做径向量函数，标准的一般使用欧氏距离，尽管其他距离函数也是可以的。一些径向函数代表性的用到近似给定的函数，这种近似可以被解释成一个简单的神经网络，径向基函数在支持向量机中也被用做核函数。-Radial basis function depends only on the valu
Untitled
- 产生一512点的随即序列xe(n)并用xc(n)和 xe(n)做线形卷积，观察卷积前后xe(n) 频谱的变化。要求将xe(n)分成8段，采用重叠相加法-512 point sequence Xe (n) and XC (n) and Xe (n) to do linear convolution, the Xe (n) spectrum changes before and after convolution is observed. Xe (n) is divided into 8 secti
Untitled2
- 产生一512点的随即序列xe(n)并用xc(n)和 xe(n)做线形卷积，观察卷积前后xe(n) 频谱的变化。要求将xe(n)分成8段，采用重叠保留法。-512 point sequence Xe (n) and XC (n) and Xe (n) to do linear convolution, the Xe (n) spectrum changes before and after convolution is observed. Xe (n) is divided into 8 segm