模糊控制mppt

基于Matlab的二自由度车辆的动力学中平顺性仿真(Ride Comfort Simulation of Two-DOF Vehicle Dynamics Based on MATLAB)

说明：  多元经验模式分解（MEMD）算法是EMD算法从单个变量到任意数量的变量的扩展，其与经验模态分解一样存在模式混合问题，基于噪声辅助的多元经验模式分解（NAMEMD）就在对MEMD的改进，解决其问题。(The multiple empirical mode decomposition (MEMD) algorithm is an extension of the EMD algorithm from a single variable to any number of variables. Like the empirical mode decomposition, there is a mode mixing problem. The noise-assisted multiple empirical mode decomposition (NAMEMD) is just Improve and solve its problems.)

本人为朋友在蓝光地产做财务做的应用软件，实现用户同银行财务数据比对使用-himself as a friend of the blue estate so do financial application software, users of financial information with the banks over the use of

智能优化算法及其在机械工程中的应用，该书介绍最新群体智能算法在机械工程的应用(Intelligent optimization algorithm and its application in mechanical engineering, the book describes the latest swarm intelligence algorithm in mechanical engineering applications)

protues仿真基于DS1820多点粮食监控,液晶显示检测到的温度-protues simulation based on DS1820 more food monitoring, LCD temperature detected

说明：  misp，五级流水源码，实现一个建议的cpu(Misp, five-stage flow source code, implementation of a recommended CPU)

决定你一生成就的21个信念,决定你一生成就的21个信念决 定你一生成就的21个信念.-decision of your life achievements 21 convictions, deciding your lifetime achievements of the 21 convictions decision achievements of your life 21 convictions decision achievements of your life 21 convictions.

C#写的俄罗斯方块 -C# Written Tetris C# Written Tetris

共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(The Conjugate Gradient method is a method between the steepest descent method and the Newton method. It only needs to use the first derivative information, but overcomes the shortcoming of the steepest descent method and avoids the need for the Newton method to store. And the disadvantage of calculating the Hesse matrix and inverting, the conjugate gradient method is not only one of the most useful methods for solving large linear equations, but also one of the most effective algorithms for solving large-scale nonlinear optimization. Among various optimization algorithms, the conjugate gradient method is a very important one. The advantage is that the required amount of storage is small, step convergence, high stability, and no external parameters are required.)

说明：  电力系统潮流计算，前推回代法。自动识别网络拓扑，可改节点参数。(Power flow calculation of power system)