qpsk-error

IGBT的boost电路，最简单的，适合初学者(IGBT in the boost circuit, the most simple, suitable for beginners)

matlab 编程一百例，为 MATLAB新手准备(matlab programming 100 cases, prepared for the novice MATLAB)

短时傅里叶变换可以用差分模式来实现，对书上的运算式做了稍许的改变。改变-计算差分序列的第一列数据时用的是第一列的元素减去当前行的最后一列元素。原算法中采用的是第一列元素减去下一行第二列元素。原算法加窗之后结果比较好，不加窗时结果误差很大，而改变之后的算法不加窗效果依然很好。(Short time Fourier transform can be used to achieve the differential mode of expression on the book made a slight change. Change- calculating the difference sequence of the data with the first column is the first column of the matrix by subtracting the current element in the last column of row. Is used in the original algorithm subtracts the elements of the first column of the next row and the second column elements. After the results of the original algorithm windowed better, when the results window without great error, and after changing algorithms no window effect is still very good.)

关于小波的matlab四种变换，研究小波应用的很需要(on four Matlab wavelet transform, the application of wavelet research is needed)

MATLAB GUIDE TO BEGINNERS

这是一本描述MATLAB应用的书籍，里面内容很全面，值得一看(This is a description of the application of MATLAB books, which is very comprehensive and worth a visit)

基于改进的PI模型对非线性曲线进行拟合，二次寻优算法进行参数辨识，用逆模型前馈补偿(Based on the improved PI model, the nonlinear curve is fitted, and the second optimization algorithm is used to identify the parameters, and the inverse model feedforward compensation is used.)

说明：  基于MATLAB的反问题程序，用于反问题求解。(MATLAB program based on the inverse problem for the inverse problem solution.)

这是一个用MATLAB编写的大地测量的程序，对学习大地测量这门课很有帮助(This is a MATLAB program written in geodesy, geodetic surveying for this course helpful in learning)

用镜像矩阵求出矩阵的QR分解（正交三角分解），并求解线性方程组，该方法不必选主元，但其计算过程非常稳定。也可用于求矩阵的广义逆和求解线性最小二乘问题。子过程QRDCMP用镜像矩阵求m*n矩阵A的QR分解，A=Q^TR,其中Q是m*m正交矩阵，R为m*n上三角矩阵；子过程QRBKSB用矩阵的QR分解求解线性方程组Ax=b，其中A为n阶非奇异方阵。(Matrix obtained with the mirror matrix QR decomposition (orthogonal triangular decomposition), and solving linear equations, the method does not have to choose the main element, but its calculation process is very stable. Can also be used to find the generalized inverse matrix and solving linear least squares problems. Sub-process of the mirror matrix QRDCMP m* n matrix A QR decomposition, A = Q ^ TR, wherein Q is a m* m orthogonal matrix, R is m* n upper triangular matrix subprocess QRBKSB a QR decomposition to solve the matrix linear equations Ax = b, where A is non-singular square matrix of order n.)