FEKO-5.3-exmaple

说明：  基于动力学学模型的轨迹跟踪控制，无人驾驶车辆模型预测控制第二版第五章(Trajectory tracking control based on dynamics model, Chapter 5 of model predictive control of driverless vehicle)

对微电网进行潮流计算，四用于直流和交流系统(power flow calculation)

Java Script & VB Script 教程(Java Script Guide VB Script)

说明：  精通MATLAB最优化计算 配套光盘 本书的主要内容是应用MATLAB来解决最优化问题，通过将“最优化问题”、“MATLAB优化工具箱”和“MATLAB编程”这三方面有机结合进行讲述，即一方面是使用工具箱来快速解决最优化问题，另一方面是通过算法编程深入解决最优化问题。主要包括MATLAB优化工具箱、无约束多维极值问题、约束优化问题、非线性最小二乘优化问题、线性规划、整数规划、二次规划、粒子群优化算法、遗传算法。 (The main contents of the book is the application of MATLAB to solve the optimization problem, by &quot optimization problem&quot , &quot MATLAB Optimization Toolbox&quot and &quot MATLAB Programming&quot combination of these three were about, that one is using the kit to quickly solve the optimization problem, on the other hand, through in-depth programming algorithm to solve the optimization problem. Including MATLAB optimization toolbox, unconstrained multi-dimensional extremum problems, constrained optimization, nonlinear least squares optimization problem, linear programming, integer programming, quadratic programming, particle swarm optimization, genetic algorithm.)

书中详细介绍了地震勘探中的一些非常重要的数值计算方法，并用matlab将其算法实现，有助于理解地震勘探的基本原理 。(The book details some of the most important numerical calculation methods in seismic exploration, and uses Matlab to implement its algorithm, which is helpful to understand the basic principles of seismic exploration.)

用于频谱测量的非常好的labview程序，运行无误。(Spectrum measurements for a very good LabVIEW program, run correctly.)

说明：  《MATLAB R2016a数字图像处理34例》张德丰 著(34 cases of MATLAB r2016a digital image processing by Zhang Defeng)

同步发电机空载三相两项短路情况下的各相波形以及励磁电流波形(Three-phase synchronous generator no-load short-circuit in case of two-phase waveform and the excitation current waveform)

GPS和INS组合导航MATLAB程序，融合状态向量为平台误差角、位置误差、速度误差、陀螺仪漂移、陀螺仪马尔科夫偏差、加速度漂移共18维向量；测量采用速度-位置法；含有数据(GPS/INS fusion by kalman filter)

可以得到很精确的幅值、频率、相位估计，用于信号特征提取、信号消噪，有循环检测，周期性检测。( You can get a very accurate amplitude, frequency, phase estimation, For feature extraction, signal de-noising, There are cycle detection, periodic testing.)