im_sim_model(1)

Matlab Code for signal Generation form human thoughts

潮流计算程序,用Ｍ语言实现简单电力系统潮流计算，已通过编译(powerflow)

能够实现基二的fft的实现，很好用，亲自试用(Base fft implementation, useful, and personally trial)

关于机器学习的matlab范例，最好多执行，尤其可以通过一段一段的执行脚本来更好地理解机器学习是如何处理经典问题的(MachineLearning.m is the main script that needs to be d. You can this script section-by-section. It demostrates the use of different machine learning techniques for a classification problem.)

在MATLAB中用小波变换实现边缘检测，具有一定的参考价值。(Wavelet transform used in MATLAB to achieve edge detection, has a certain reference value.)

对卡尔曼移动火车速度的仿真和经典卡尔曼仿真(The speed of moving trains on the Kalman simulation and simulation of the classic Kalman)

二维数据平滑，跟定二维数据，每9个点拟合，中间点带入拟合的曲线，得到平滑后的数据(2 d data smoothing, with 2 d data, fitting every nine points, intermediate points to the fitting curve, get data after smoothing)

运用matlab编写的密码登录窗口 亲测有效(Password login window written by Matlab is valid)

统的联合概率数据关联算法(IPDA)是在密集杂波环境下的一种良好的多目标跟踪算法， 但它是针对单传感嚣对多目标跟踪的情况下使用，不能直接用于多传感器对多目标的跟踪。针对这 一问题，文中提出了一种适用于多传感器多目标跟踪的JPDA算法，它以极大似然估计完成对来自 多传感器的测量集合进行同源最优分划，然后采用JPDA方法对多目标进行跟踪。经过理论分析和 仿真试验，证明了该方法能有效地进行多传感器多目标的跟踪，且具有算法简单、跟踪精度高、附 加的计算量小等优点。(Abstract)m JointProbabilisticDataAssociation(JPDA)solves single sensormultitatgcttrackingin ClUtter,butitc锄not bc used directly in mnitisensor multitarget tracking．喇8 paper presents a method to implement multi—sensor multi—target tracking by combining maximum likelihood estimation wim JPDA The maximum Iikelihood estimation is used to classify the same source observations at one time into the same set,and血en JPDA implements multi—target uacking after the position fitsed．The theory analysis and computer simulation show that this algorithm may achieve multi—sensor multi—target tracking perfectly with low calculation load added and higher precision 。)

Burgers方程作为流体计算中的经典模型方程之一，他的理论解作为验证求解格式的重要依据！(Burgers equation as a calculation of the classical fluid model equations, one of him as a validation of the theoretical solution to solve an important basis for the format!)