装饰模式-基本代码

用户登陆This function is leting user load,but it is not very useful and very common-This function is leting user load,but it is not very useful and very common

计算方法 用高斯消元法解方程组，源代码以及注释 注释在源代码中-calculated using Gaussian Elimination Solution equations, the source code and notes in the Notes source code

说明：  给定基于MP模型的的模拟PA，采用RLS算法辨识参数，并构建PA，在使用基于MP模型的DPD对PA实现非线性的改善。(Given the simulated PA based on MP model, RLS algorithm is used to identify the parameters, and PA is constructed. DPD based on MP model is used to improve the nonlinearity of PA.)

红外编码发射器+红外解码器完整方案,对新手很有借鉴意义。(Infrared Transmitter+ IR encoder decoder full program, meaning a very useful reference for beginners.)

说明：  可绘制三维等高线图，为广大的学生解决了绘图的难题，这有利于matlab的传播和使用(Drawing three-dimensional contour map)

CPLD_FPGA的入门资料.pdf格式,非常清晰.不可多得的入门级资料.(CPLD_FPGA introductory information. Pdf format, is very clear. A rare entry-level information.)

说明：  可以用于计算平板问题单元刚度矩阵、应力应变的分布情况等等。(Calculation of element stiffness matrix for plate problems)

《运筹学·第三版》清华大学出版社书籍书籍(Operational research, Third Edition, Tsinghua University press)

说明：  基于c#语言，利用星历数据求解卫星坐标，可以显示各个星历数据的具体含义，设置解算卫星坐标起止时刻以及计算间隔(Based on c # language, using ephemeris data to solve the satellite coordinates, you can display the specific meaning of each ephemeris data, set the start and end time of calculating the satellite coordinates and the calculation interval)

说明：  在葡萄酒制造业中，对于葡萄酒的分类具有很大意义，因为这涉及到不同种类的葡萄酒的存放以及出售价格，采用神经网络做为分类器可以有效预测相关葡萄酒的种类，从UCI数据库中得到wine数据记录的是在意大利某一地区同一区域上三种不同品种的葡萄酒的化学成分分析，数据里含有178个样本分别属于三个类别(类别标签已给)，每个样本含有13个特征分量(化学成分)，将这178个样本70%做为训练样本，另30%做为测试样本，用训练样本对神经网络分类器进行训练，用得到的模型对测试样本的进行分类标签预测。(In the wine manufacturing industry, classification of wine is of great significance, because it involves the storage and selling price of different kinds of wine. Using neural network as classifier can effectively predict the types of related wine. The wine data recorded from UCI database is the chemical composition of three different kinds of wine in the same region of Italy According to the analysis, 178 samples in the data belong to three categories (the category label has been given). Each sample contains 13 characteristic components (chemical components). 70% of the 178 samples are used as training samples, and 30% are used as test samples. The training samples are used to train the neural network classifier, and the model is used to predict the classification label of the test samples.)