铰接薄壳的后屈曲分析

该模型是对50kW、380V、50Hz三相三电平逆变器的两个相同电路进行建模。IGBT逆变器采用SPWM技术，(8kHz载频)将直流电源从a +/-200Vdc源转换为220V AC, 50Hz。逆变器通过75kVA 220/380V变压器输入50kW电阻负载。变频器输出采用L-C滤波器滤除主要产生于8kHz开关频率倍数(Fh=n*8000 +/-k*50Hz)附近的谐波频率Fh。逆变器所需的12个逆变脉冲由PWM发生器块产生。该系统以恒定的调制指标在开环中工作。 电路1使用三电平桥块对逆变器进行建模。电路2使用独立的IGBTs和二极管块。用开/关延时块模拟死区，仅在电路1上应用。(It is easy for us)

说明：  GRNN网络的预测——基于广义回归神经网络的货运量预测(Prediction of GRNN Network-- Freight Volume Forecast based on Generalized regression Neural Network)

说明：  电力系统电压调节及一次调频的MATLAB仿真(Matlab simulation of primary frequency regulation)

说明：  LOS制导方式实现路径跟踪算法仿真，直线和曲线的仿真代码(Los guidance method to achieve path tracking algorithm simulation, straight line and curve simulation code)

Intelligent Microgrid

大坝变形的精确预测对其安全评价方面发挥着重要作用。为了进一步提高大坝变形预测精度，提出了一种改进的灰狼算法优化支持向量机的预测模型。通过引入非线性收敛因子和采用动态加权策略，提升灰狼算法优化支持向量机惩罚因子和核函数参数的能力，并以优化的参数来建立支持向量机大坝变形预测模型。选取实例数据，在与布谷鸟算法、差分进化算法、粒子群算法、基本灰狼算法优化的支持向量机预测比较中，实验结果表明，改进的灰狼算法对支持向量机参数的优化是有效的，可以提高大坝变形预测精度。(Accurate prediction of dam deformation plays an important role in its safety evaluation. In order to further improve the prediction accuracy of dam deformation, an improved grey Wolf algorithm is proposed to optimize the prediction model of support vector machine. By introducing the nonlinear convergence factor and using the dynamic weighting strategy, the gray wolf algorithm is improved to optimize the capacity of the support vector machine penalty factor and the kernel parameter, and the support vector machine dam deformation prediction model is established with the optimized parameters. The experimental results show that the Improved Grey Wolf algorithm is effective for the optimization of support vector machine parameters, and it can improve the accuracy of the deformation prediction of the dam.)

说明：  主要用于ansys workbench提取结构件的刚度矩阵，希望能帮助到有需要的人(It is mainly used in ANSYS Workbench to extract the stiffness matrix of structural parts, hoping to help people in need)

这是一本包含了机器人与机械臂建模以及仿真的书籍，具有很高的参考价值！(This is a book which includes the modeling and Simulation of robots and manipulators. It has high reference value.)

提供非线性自回归神经网络（NAR）的训练及前向预测代码(Training and Forward-Predictive Code of nar Neural Network)

实测可用的PV特性分析，增加几个个人搜集的扰动观察法代码，可以共同学习研究(The available PV characteristic analysis of the actual measurement can be added with the disturbance observation code collected by several individuals, which can be studied together)