xiaobo

最小二乘曲线拟合 可以拟合曲线 得到所拟合的系数 GUI界面(least-square Curve fitting)

说明：  信号幅度调制与解调 和 希尔伯特变换与单边带幅度调制 (Amplitude modulation and demodulation and Hilbert Transform and single sideband amplitude modulation)

马尔科夫蒙特卡洛算法的MATLAB应用示例(code for MCMC in MATLAB)

DFT内插 比值法校正 能量重心校正 相位校正等MATLAB 源程序 可直接仿真应用(Code based on MATLAB simulation It is very good button source code integrity, and can be used directly)

说明：  binary particle swarm optimization for features selection

matlab coce for generate MSK modulation.

This program graphics a signal with all colours and make the analysis in the frequency domain.

NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法，它是在网络分析法（AHP）基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个： 1 确定因素，并建立网络层和控制层模型。 2 创建比较矩阵。 3 按照指标类型针对每列进行规范化。 4 求出每个比较矩阵的最大特征值和对应的特征向量。 5 一致性检验。如果不满足，则调整相应的比较矩阵中的元素。 6 将各个特征向量单位化(归一化)，组成判断矩阵。 7 将控制层的判断矩阵和网络层的判断矩阵相乘，得到加权超矩阵。 8 将加权超矩阵单位化(归一化)，求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。 (NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following: A determining factor, and a network layer and control layer model. 2 create a comparison matrix. For each of the three types of indicators in accordance with normalized columns. 4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors. 5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements. 6 will each feature vector units of (normalized), to determine the composition of matrix. 7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix. 8 of the weighted super-matrix units of (normalized), seeking the powe)

说明：  介绍关于fastica的一组matlab代码，很实用(Describes a group of about fastica matlab code, very useful)

线性调频信号雷达脉冲压缩仿真及程序，chirp信号的产生，压缩及效果(LFM singal simulation)