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这是一种二阶盲源分离算法，算法速度快，程序简单。(This is a second-order blind source separation algorithm, the algorithm fast, simple procedure.)

核主成分分析是一种流行的非线性特征提取方法( Kernel principal component analysis is a popular nonlinear feature extraction method)

抛物线法求解 方程的构造方法:给出[0,1]区间上的随机数(服从均匀分布)作为方程的根p*. 设你的班级数为a3,学号的后两位数分别为a2与a1,从而得到你的三次方程 例如:你的31班的12号,则你的方程是21x3+60x2+2x+a0=0的形式. 方程中的系数a0由你得到的根p*来确定.(parabolic equation method Construction Methods : given interval [0,1] on the random number (subject to uniform distribution) as the root equation p*. set up your classes at a3, after learning of the double-digit for a1 and a2, so you get the three equations for example : your 31 classes on the 12th, then your equation is 21x3 60x2 2x a0 = 0 forms. the equation coefficients a0 you get from the root of p* to determine.)

说明：  matlab 编写的gps/sins组合导航程序,附程序说明与数据输入文件(matlab prepared gps/sins navigation procedures, with explanation of the procedures and data input file)

说明：  This procedure realize the LPP dimension reduction function, used for fault diagnosis of multimodal, object for CSTH (This procedure realize the LPP dimension reduction function, used for fault diagnosis of multimodal, object for CSTH)

单相半波整流电路谐波分析，可以计算谐波的幅值，谐波含量并画图。(Single-phase half-wave rectifier circuit harmonic analysis, we can calculate the magnitude of the harmonics, harmonic content and Paint.)

矩阵交织器和解交织器，实现通信系统中比特信号的交织与解交织。(Matrix interleaver and deinterleaver, to achieve interleaving and deinterleaving of information bits in a communication system.)

说明：  信号的希尔伯特变换 可以提取信号的顺时针夫 瞬时相位 瞬时频率(Hilbert transform of signal can extract the instantaneous frequency of clockwise instantaneous phase of signal)

高效的数值计算及符号计算功能； 实现基本信号的表示及可视化计算； 分析信号的频谱。 (Efficient numerical computation and symbolic computation function the realization of the basic signal and visualization of that calculation analysis of the signal spectrum.)

非平衡数据集的分类问题经常出现在许多实际应用中.支持向量机在处理这一类问题时,整体分类性能比较低.为此,Veropoulos提出的采用不同惩罚系数的改进算法可以较好的解决此类问题.此外,可以利用序列最小优化算法简单快速的解决上述优化问题.(Non-equilibrium data sets classification problems often appear in many practical applications. Support Vector Machine in dealing with this type of issue, the overall classification performance is relatively low. To this end, Veropoulos proposed punishment coefficient using different algorithms can improve the better solution such issues. In addition, can use the Sequential Minimal Optimization algorithm is simple and fast solution to the above optimization problem.)