HMM-1.0

matlab 计算带驱动力偏微分方程poincare map，方程为dy(1)=y(2) dy(2)=-k*y(2)-y(1)^3+B*cos(t) (Matlab calculation of belt driving force of partial differential equation and Poincare map equation, Dy (1) =y (2) Dy (2) =-k*y (2)-y (1) ^3+B*cos (t) )

迪杰斯特拉算法求最短路径matlab的实现算法(Dijkstra shortest path algorithm)

微分方程求解 (Differential equation solution )

mage compression is one of the most important step in image transmission and storage. Most of the state-of-art image compression techniques are spatial based. In this code, a histogram based image compression technique is implemented based on multi-level image thresholding. The gray scale of the image is divided into crisp group of probabilistic partition

恒参信道对信号的影响。包含具体例子，信号为自编。(Constant and channel the influence of the signal.)

有关机载传感器管理、传感器资源分配的经典外文博士论文，很值得仔细读读哦。(On the airborne sensor management, sensor resource allocation classic foreign doctoral thesis, is worth reading carefully Oh.)

多元测试信息融合课程的编程作业，利用DS理论进行数据融合，有相关的例题(Multivariate testing information fusion programming assignments courses use DS theory for data fusion, there are relevant examples)

extracting the background

利用matlab模拟光纤中EH11模的模式分布图。(Using matlab simulation mode fiber EH11 mode maps.)

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.)