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针对机械优化设计的matlab优化M文件集,非常有用(For optimization of mechanical optimization design matlab M-file set, very useful)

通信系统仿真的星座图的画法问题，qpsk的仿真，图像很准确(Communication System Simulation constellation of painting issues, qpsk simulation, the image is very accurate and)

说明：  北航刘金锟老师的《机器人控制系统设计与MATLAB仿真》第二章和第三章附带的部分完整matlab源程序,我每个都仿真学习过了！(Beihang Liu Kun teachers, &quot Robot Control System Design and MATLAB simulation,&quot Chapters II and III with the part of the complete matlab source code, I studied a simulation of each of them!)

进行RS分析的经典算法，用matlab编写(Analysis of the classical algorithm RS, written by MATLAB )

粒子群算法求解车辆路径优化问题，有需要的可以参考一下(PSO for Vehicle Routing Problem)

阵列信号处理常用的MUSIC算法，包括经典MUSIC，求根MUSIC，基于四阶累积量的MUSIC等(MUSIC usual array signal processing algorithms, including the classic MUSIC, Root MUSIC, based on fourth-order cumulant MUSIC etc.)

通过传统互相关的方法来进行声源定位的程序,最后实现延迟差的估计(Estimated by the traditional method of cross-correlation to the sound source localization process, the final realization of the delay difference)

数学建模的程序，用于绘制多元因素的图，2011年建模的程序(Mathematical modeling procedures, multiple factors used to draw maps, 2011 modeling procedures)

星历计算 可以自己调试运行下 很好的一个matlab程序 根据一本书编写的(Ephemeris calculation a good matlab programme)

极限学习机（extreme learning machine）ELM是一种简单易用、有效的单隐层前馈神经网络SLFNs学习算法。2006年由南洋理工大学黄广斌副教授提出。传统的神经网络学习算法（如BP算法）需要人为设置大量的网络训练参数，并且很容易产生局部最优解。极限学习机只需要设置网络的隐层节点个数，在算法执行过程中不需要调整网络的输入权值以及隐元的偏置，并且产生唯一的最优解，因此具有学习速度快且泛化性能好的优点。(Extreme Learning Machine (extreme learning machine) ELM is an easy-to-use and effective single hidden layer feedforward neural network the SLFNs learning algorithm. 2006 by the Nanyang Technological University Associate Professor Huang Guangbin. Traditional neural network learning algorithm (BP) artificial network training parameters, and it is easy to generate a local optimal solution. Extreme Learning Machine network only need to set the number of hidden nodes, the algorithm implementation process does not need to adjust the network input weights and hidden element of bias, and only optimal solution, so the learning speed and generalization good performance advantages.)