logpolar

扩展卡尔曼轨迹追踪的仿真程序，输出目标的实际位置，及误差曲线。(Extended kalman tracking simulation program, the actual position of the target output, and error curve. )

《matlab在数学建模中的应用》一书 所有的 源代码(All the source code of the book " matlab mathematical modeling)

说明：  基于遗传算法的机械故障诊断，可以更好的对故障进行分离，使得结果更准确。使用该程序优化效率更高，相对于传统的优化算法，该算法运算速度更快。(Mechanical fault diagnosis based on genetic algorithms, you can better separate the fault, making the results more accurate. More efficient use of the optimization program, as opposed to traditional optimization algorithm computing faster.)

阵列平面为面阵列，延时求和算法，完全自己书写(Planar array for planar array, delay sum algorithm, his writing completely)

卡尔曼滤波算法的一网打尽，包括各种实现的matlab代码(Kalman filter algorithm a clean sweep, including implementation of the matlab code)

现代通信系统matlab版（中文）用超星打开(Modern communications systems matlab version (Chinese) open with Chaoxing)

可逆跳跃马尔科夫蒙特卡洛贝叶斯模型选择，主要用于神经网络(Reversible Jump MCMC Bayesian Model Selection This demo demonstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.)

MATLAB Stellaria Demo APPS

说明：  广义S变换，用于时频分析，是S的改进形式(Generalized S-transform, used in time-frequency analysis, is an improved form of S)

The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order (The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order)