matlabddfz

some code related to jeeps

仿真（ZF,ZF-SIC,ZF-OSIC,MMSE,MMSE-SIC,MMSE-OSIC,ML)Vblast接收机的检测性能，绘制误比特率～信噪比曲线(Simulation (ZF, ZF-SIC, ZF-OSIC, MMSE, MMSE-SIC, MMSE-OSIC, ML) detection performance Vblast receiver bit error rate ~ SNR plotted curve)

matlab msk modulator + gaussian filtr and GMSK modulator

DTMF Decoder model for Matlab simulink. Using Evans algorithm.

这是一个基于Matlab语言绘画三角函数图形的源代码(This is a painting based on Matlab language source code for trigonometric graph)

erweew rwe rweumior mjwioej w jemiormjweojr io wmeiojriowejm oiwmh wejhr iwmh rhw mweh ri

拉格朗日插值，进行5,10,20点插值，验证插值具有振荡现象(Lagrange interpolation for 5,10,20 point interpolation, interpolation with oscillation phenomenon verification)

说明：  生成正态性检验及界面,可进行完整的正态性检验(Normal test generation and interface, can be a complete test of normality)

说明：  LLDA，局部线性判别分析，可以降到任意的维度，相比LDA更加优秀(LLDA, Local Linear Discriminant Analysis, can be reduced to any dimension, which is better than LDA.)

Rosenbrock函数优化属于无约束函数优化问题，其全局极小值位于一条平滑而狭长的抛物线形状的山谷底部，且为优化算法提供的信息很少，因此找到其全局极小值就显得很困难。根据Rosenbrock函数的这种特性，专门提出了一种改进的PSO算法，该算法引入三角函数因子，利用三角函数具有的周期振荡性，使每个粒子获得较强的振荡性，扩大每个粒子的搜索空间，引导粒子向全局极小值附近靠近，避免算法过早地收敛，陷入局部最优，从而找到Rosenbrock函数的全局极小值。大量实验结果表明，该算法具有很好的优化性能，为某些领域某些特定的类似于Rosenbrock函数的优化问题提供了一种新的思路。(Rosenbrock function optimization functions are unconstrained optimization problems, its global minimum value at a smooth parabolic shape and narrow mountain valley Department, and for the optimization algorithm provides little information, and therefore find its global minimum value becomes very difficult. According to this feature Rosenbrock function, specifically mentioning Out an improved PSO algorithm (PSO-R), the algorithm introduced trigonometric factor, using trigonometric has periodic oscillations, so that each particle to obtain more Strong oscillations of expanding the search space of each particle, the particle is close to nearby guide global minimum, avoiding premature convergence algorithm, into a local optimum, thus Finding the global minimum Rosenbrock function. Experimental results show that the algorithm has a good optimizing performance, similar to some certain areas Rosenbrock function in the optimization problem provides a new way of thinking.)