实时车辆车道检测 matlab 代码

simulink的仿真有两个阶段：一个为初始化，这个阶段主要是设置一些参数，像系统的输入输出个数、状态初值、采样时间等；第二个阶段就是运行阶段，这个 阶段里要进行计算输出、更新离散状态、计算连续状态等等，这个阶段需要反复运行，直至结束。(simulink simulation has two phases: one for initialization, this stage is mainly to set some parameters, such as the number of input and output system, initial state, the sampling time second stage is the operational phase, the Stages to calculate the output, update the discrete state, continuous calculation of the state, etc., this stage requires repeated runs to the end.)

Genetic Algorithm for mono-objective problems

Power Quality Improvement and Mitigation Case Study Using Distributed Power Flow Controller

对EMD分解方法每一步的结果展示，包括求包络线、取平均值、减平均值后再求包络线，最后得IMF分量(EMD decomposition results for each step of the show, including seeking envelope, averaged, and then find the average value minus the envelope, and finally get IMF component)

我们数字信号处理课程设计的所有内容的最终版本内附总结(We design digital signal processing course the final version containing all the content summary)

最好QPSO，该算法从随机解出发,通过迭代寻找最优解,并通过适应度来评价解的优劣。这种算法以其参数少、形式简单、精度高、收敛快等优点

Implementation of Marr-Poggio-Grimson stereo pair matching. The demonstration for random dot stereopair is included.

小世界网络的matlab仿真文档，用于复杂网络研究(Small world network of matlab simulation document, used for complex network of research)

计算时间和二维直方图,利用贝叶斯原理估计混合logit模型的参数，DC-DC部分采用定功率单环控制。( Computing time and two-dimensional histogram, Bayesian parameter estimation principle mixed logit model, DC-DC power single-part set-loop control.)

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.)