matlab

说明：  交互多模型算法，用于目标跟踪等，是目前比较理想的算法之一(Interacting multiple model algorithm)

reading of lettrrs on page

File with compression factor of the particle swarm algorithm, the weight to improve particle swarm optimization, adaptive weighting method, the random weighting method, particle swarm optimization variable learning factor, learning factor of asynchronous change, the second-order particle swarm optimization, second-order oscillating particle swarm algorithm, chaotic particle swarm optimization, hybrid particle swarm optimization, hybrid particle swarm optimization, simulated annealing algorithm

this is aircraft power network cotrol system.

MIT-BIH CUDB 详细心电信号，可用于分类VT/VF 与 NSR(MIT-BIH CUDB detailed ECG can be used to classify VT/VF and NSR)

阵列声源声场模拟的matlab程序（包括声柱，同相点声源等）( simulation of spk arriys’acoustic performance )

对单机无穷大系统仿真研究，对故障类型设计，判别(Research on the simulation of single machine infinite bus system)

％％％ Demos for PUMA algorithms ％％％ We present four matlab demos for PUMA. demo1, demo2, demo3, and demo4 illustrate PUMA working with different parameters and with four different images. All you need to do is to run each of the demos. Please be sure that all the files are put on an accessible path for matlab. Notice that this code is intended for research purposes only. For further reference see "Phase Unwrapping via Graph Cuts, IEEE Transactions on Image Processing, 2007( Demos for PUMA algorithms We present four matlab demos for PUMA. Demo1, demo2, demo3, and demo4 illustrate PUMA working with different parameters and with four different images.All you need to do is to run each of the demos . Please be sure thatall the files are put on an accessible path for matlab.Notice that this code is intended for research purposes only.For further reference see )

此程序是粒子滤波和扩展卡尔曼滤波器的比较，具有很强的针对性(This procedure is the comparison of particle filtering and extended Kalman filter, is highly targeted)

这是一个典型的流形学习的算法，算法为目前比较经典的一个，可以参考学习流形学习相关的东西(This is a typical manifold learning algorithm, the algorithm can refer to a more classic, the learning manifold learning things)