surf-feature-by-mySelf

主要研究了比较经典的直接加窗加权旁瓣抑制技术，并利用Matlab进行了仿真，给出了对线性调频信号和 相位编码信号脉冲压缩旁瓣抑制的直接加窗加权法处理结果，得出了分析结论。(Mainly studied the classical direct windowed weighted sidelobe suppression techniques, and using the Matlab simulation, given a linear FM signal and The phase-encoded signal pulse compression sidelobe suppression of the direct plus window weighting method the results of the analysis conclusions.)

Simulation Antennas_UWB beamforming

判决反馈均衡器，前馈滤波器在频域实现，反馈消除干扰(DFE equaliter ，the forward filter is in frequency domain ，the feedback filter can estimate ISI )

关于网络仿真生成trace后文件的分析代码(After about network simulation generates trace file analysis code)

说明：  蚁群算法，云模型，逻辑斯蒂模型，SVM超平面聚类分析(Ant colony algorithm, cloud model, logistic model, SVM hyperplane clustering analysis)

全选主元消元法解方程组，通过测试，可以自己修改方程组(Select principal component elimination method solution of equations)

第1章MATLAB操作基础 第2章MATLAB矩阵及其运算 第3章MATLAB程序设计 第4章MATLAB文件操作(Chapter 1 MATLAB operation based on MATLAB matrix in Chapter 2 and Chapter 3 of computing MATLAB Programming Chapter 4 MATLAB file operations)

捷联惯导航姿系统C语言实现，可以方便的得到飞机的飞行姿态。(Navigation position system SIMU program development, you can easily get the aircraft attitude.)

利用matlab环境设计了DEA方法中CCR模型(Environmental design using matlab CCR DEA model )

基于最小二乘支持向量机理论,结合某风电场实测风速数据,建立了最小二乘支持向量机风速预测模型。对该风电场的风速进行了提前1h的预测,其预测的平均绝对百分比误差仅为8.55 ,预测效果比较理想。同时将文中的风速预测模型与神经网络理论、支持向量机(support vector machine,SVM)理论建立的风速预测模型进行了比较。仿真结果表明,文中所提模型在预测精度和运算速度上皆优于其他模型。 (Based on least squares support vector machine theory, combined with a wind farm measured wind speed data, the establishment of a wind vector machine forecasting model of least squares support. The velocity of the wind farm were predicted in advance 1h, the mean absolute percentage error of only 8.55 of its forecast, forecast effect is ideal. While the text of the wind speed forecasting models and neural networks, support vector machines (support vector machine, SVM) wind speed prediction models were compared with established theories. Simulation results show that our proposed model on prediction accuracy and computing speed are superior to other models.)