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经验模式分解程序（matlab）可以对输入信号进行经验模式分解(Empirical mode decomposition process (matlab))

matlab gui 设计的基本实现，基于matlab simulink 的设计(matlab)

new technology to classify the wave file using neural network

用GA算法和ACO算法在MATLAB上解决TSP问题，附有31个城市的坐标数据(Coordinate data with GA algorithms and ACO algorithm in MATLAB to solve the TSP with 31 cities)

MUSIC算法是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。MUSIC算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱，所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率，同时适应于任意形状的天线阵列，但是原型MUSIC算法要求来波信号是不相干的。(MUSIC algorithm is a matrix decomposition method based on feature space. From the geometric point of view, the observed spatial signal processing can be decomposed into signal subspace and noise subspace, it is clear that the two spaces are orthogonal. The signal received by the array to the subspace of the covariance matrix of the signal component corresponding eigenvectors, the noise subspace of the covariance matrix of all by the smallest eigenvalue (noise variance) eigenvectors corresponding to the composition. MUSIC algorithm is the use of orthogonal properties between these two complementary space to estimate the spatial orientation of the signal. Noise subspace of all vectors are used to construct the spectrum, all the spatial orientation of the spectrum corresponding to the peak position of the signal wave direction. MUSIC algorithm greatly improves the resolution measurements, while the antenna array adapted to any shape, but the prototype MUSIC algorithm requires to wave sign)

模式识别与智能计算-MATLAB技术实现(Pattern Recognition and Intelligent Computing-MATLAB technology)

OFDM At AWGN CHANEL

test1.m 固定增量法求分界面 test2.m 利用误差平方和函数集群 delet.m 删除数组元素 Err.m 计算误差平方和 Distance.m 欧拉距离计算 (test1.m fixed incremental method for use interface test2.m squared error function cluster de let.m delete array elements Err.m calculation error square Distance.m Euler distance)

电动汽车充电功率需求的统计学建模方法，可以看看喔(Electric vehicle charging power requirements of the statistical modeling methods, you can see Oh)

一个较为广泛的离散小波变换的实现方法，值得新手练习使用。(A more extensive implementation of discrete wavelet transform, worth novice practice use.)