181704220922255

说明：  用amra实现的风电功率预测，代码可直接运行(Wind power forecasting realized by amra, the code can run directly)

说明：  直流配网模型，非常基础的直流配网模型，便于对直流配网的基本掌握和学习(DC distribution network model, very basic DC distribution network model, easy to grasp and learn the basic DC distribution network)

基于matlab搭载的F16动力学分析程序(F16 dynamic analysis program based on MATLAB)

说明：  本模型采用abaqus自带的connector连接单元建模，实现了钉节点的模拟(In this model, the connector unit of ABAQUS is used to simulate the nail node)

说明：  基于遗传算法的N×N阵列可重构天线的建模仿真脚本(genetic algorithm is applied to M N reconfigurable antenna array)

节点度是指和该节点相关联的边的条数，又称关联度。 特别地，对于有向图， 节点的入度 是指进入该节点的边的条数; 节点的出度是指从该节点出发的边的条数。(Node degree refers to the number of edges associated with the node, also known as the degree of association. In particular, for a directed graph, The ingress of a node is the number of edges entering the node; The out-degree of a node is the number of edges that start from that node.)

说明：  用C语言将相变温度-压力曲线写入udf，用于fluent中自定义函数功能，添加于物性参数。(Phase Transition Temperature-Pressure Curve)

说明：  功角曲线测试实验的PSCAD模型,极限切除角实验,等面积法则(Test experiment of power angle curve)

通过carsim与matlab联合仿真，基于模型预测控制算法的无人车辆轨迹跟踪（内含五个matlab编程代码）(Through simulation of CarSim and MATLAB, achieving trajectory tracking control of intelligent automotive based on MPC (containing five matlab programming code))

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.)