crossvalind

说明：  test code for the previous code

目标跟踪算法源代码，效果挺好，没错误。从事这方面学习的，可参考以下(Target tracking algorithm source code, the effect is very good, no errors. Engaged in this study, refer to the following)

A tutorial on Principal Components Analysis

Trafic project with matlab about image processing

可用於arima的計算,檢測數據以及預測用途(do arima)

改进型的蚁群算法，能够快速求解多目标规划(Improved ant colony algorithm, fast multi-objective planning)

关于距离走动和距离走动校正的仿真程序，思路清晰，很有条理，赶紧下载吧。(About distance walking distance around and calibration of the simulation program ideas clearly, very organized, hurriedly download it )

粒子群算法OptSwarm记录每个粒子的历史最优解（粒子历史最好的适应度）以及全部粒子搜索到的全局最优解。用Wg代表全局最优解，W.,1代表每个粒子的历史最优解。粒子群初始化阶段表OptSwarm的前N行与表ParSwarm中的相同，而Wg的值为表ParSwarm中适应度值的最大值对应的行。(Particle swarm algorithm OptSwarm record the history of each particle the optimal solution (particles history best fitness), and all the particles of the search to the global optimal solution. Use Wg represents the global optimal solution, w., 1 representative of each particle history to the solution. Particle swarm initial stage OptSwarm table before the line and the table ParSwarm N is same, and the value of the ParSwarm Wg for tables in the maximum fitness value corresponding line. )

This a a sphere decoding function. It has low complexity.

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