Wishart_distribution

说明：  支持向量机的MATLAB代码,包括分类与回归,可帮助学习机器学习算法(MATLAB S SVC DODE)

通信系统设计，数字带通传输系统仿真Matlab代码 1. 比较BPSK与QPSK两种调制方式的误码率和误比特率性能（注意QPSK的误码率不同于误比特率），横坐标为SNR，并与理论曲线比较，分析解释仿真图。 2. 要求使用Matlab/C语言/Matlab-simulink中的任一种工具实现。 3. 代码中每个模块都要有详细注释说明。 (Communications systems design, digital bandpass transmission system simulation Matlab code 1. Comparison of BER QPSK and BPSK modulation methods and bit error rate performance (note QPSK error rate is different bit error rate), the horizontal axis is SNR, and compared with the theoretical curve, analysis and interpretation of simulation map. 2. requires the use of Matlab/C language/Matlab-simulink any one tool to achieve. 3. The code of each module must have a detailed explanatory notes.)

半波傅氏算法，可以在半个周波内计算信号的基波及各次谐波的幅值，相角(Half-wave Fourier algorithm, can be calculated within a half cycle of the fundamental and the harmonic signal amplitude, phase angle)

Convex Optimization.----Stephen Boyd(Convex Optimization.---- Stephen Boyd)

说明：  实现交叉互联电缆的仿真模拟, 测量交叉互联电缆各相的电压电流波形(analyze the cross bonded cable)

说明：  通过matlab编程实现AP聚类算法，聚类结果输出(The AP clustering algorithm is realized by MATLAB programming, and the clustering results are output)

计算机网络技术 海明编码和CRC校验编码 matlab实现(Computer Network Technology Hamming coding and CRC check code matlab implementation)

数值分析（李乃成、梅立泉）中三次样条法matlab源程序(matlab program of spline function in numerical analysis)

说明：  共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(The Conjugate Gradient method is a method between the steepest descent method and the Newton method. It only needs to use the first derivative information, but overcomes the shortcomings of the steepest descent method and avoids the need for the Newton method to store And the calculation of Hesse matrix and the inverse of the shortcomings, conjugate gradient method is not only to solve large-scale linear equations one of the most useful methods, but also solution for large-scale nonlinear optimization of one of the most effective algorithm. In a variety of optimization algorithms, conjugate gradient method is a very important one. The advantage is that the required storage capacity is small, with step convergence, high stability, and does not require any external parameters.)

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