资源列表
Matlab-code-LCOMM-2017-master
- this paper, we study performance improvement methods for a cell-edge user of two-user non-orthogonal multiple access (NOMA) systems in downlink scenarios. To this end, we propose two cooperative relaying schemes, namely on/offfull- duplex relaying (o
Monte_Carlo
- 基于蒙特卡洛最优算法的matlab程序，以及其针对Schaffer函数和Rastrigin函数的matlab程序。(Matlab program based on Monte Carlo optimal algorithm, and its matlab program for Schaffer function and Rastrigin function.)
Burgers_equation
- The 1D Burgers equation is solved using explicit spatial discretization (upwind and central difference) with periodic boundary conditions on the domain (0,2). The 2D case is solved on a square domain of 2X2 and both explicit and implicit methods are
蝙蝠算法Bat
- 蝙蝠算法求解最优化问题，本代码是用于求解目标函数(Bat algorithm solving objective function)
lsqcurvefit.m
- %LSQCURVEFIT solves non-linear least squares problems. % LSQCURVEFIT attempts to solve problems of the form: % min sum {(FUN(X,XDATA)-YDATA).^2} where X, XDATA, YDATA and the values % X returned by FUN ca
FBCCA
- FBCCA是处理SSVEP信号重要分类识别算法(Fbcca is an important classification and recognition algorithm for processing SSVEP signals)
打包
- 两种不同的假设： H1 ： 0 xn A fn wn ( ) cos(2 ) ( ) = ++ π θ n=1,2,…,N，f0 为规一化频率 H0 ： xn wn () () = n=1,2,…,N 其中 w[n]是均值为 0，方差为 2 σ n 的高斯白噪声，A 已知，样本间相互 独立，信号与噪声相互独立; 相位θ 是随机变量，它服从均匀分布 1 0 2 ( ) 20 p θ π θ π ?? ≤ ≤ = ??? 其它 1）改变输入信噪比（改变 A 或噪声方差均可），给
用于信号的EMD、EEMD、VMD分解
- 用于信号的分解、降噪和重构，实现故障诊断(Used for signal decomposition, noise reduction and reconstruction to realize fault diagnosis)
code
- matlab实现四种最优化搜索方法 共轭梯度法 牛顿法 最速下降法 拟牛顿法 对一个十维函数的极值搜索(matlab optimal search)
seismic_colored_inversion
- 地震数据反演中的颜色反演方法，可以反演地层的波阻抗信息(Seimsic impedance inversion by using colored inversion)
数值分析
- 偏微分方程数值解法Matlab实现，包含牛顿插值法，欧拉方法等(Matlab realization of numerical solution of partial differential equation)
四节点有限元matlab
- 计算了二维问题四边形四节点的有限元问题，可以较好地解决力学实例(The finite element problem of quadrilateral four nodes in two-dimensional problem is calculated, which can solve the mechanical example well)