Distribution-plot

Simulnk动态仿真：详细介绍了在Simulnk环境下的动态仿真方法，简单易行，适合初学者(Simulnk Dynamic Simulation: Simulnk described in detail in the environment of dynamic simulation methods, is simple, suitable for beginners)

基于学习的超分辨率重构matlab代码，对于超分辨率重构的研究很有帮助(This is learning-based Super-Resolution Reconstruction matlab code. It is of very helpful for the super-resolution reconstruction )

EMD的matlab例程，用于振动、医疗、工业等领域中的信号处理(EMD Matlab routines for vibration, health, industry and other fields of signal processing)

支持向量机是用于分类与回归的技术。由于其自身的诸多缺点，如无法获得概率输出，需要估计一个误 差参数C，以及必须使用Mercer 核函数等。相关向量机算法，克服了SVM 上述缺点，RVM 能获得与SVM 相比拟的推 广性能，并且更为稀疏。在此基础上，文中介绍了一种RVM 回归用于分类的新分类方法，用RVRC 来表示。并通过 实验证明了它的可行性。(Support vector machines for classification and regression techniques. Due to its many shortcomings, such as the probability that the output can not be obtained, need to estimate an error parameter C, and the need to use Mercer kernel function and so on. Relevance vector machine algorithm to overcome these shortcomings of SVM, RVM and SVM can be obtained comparable to promote performance and more sparse. On this basis, the paper introduces a RVM regression was used to classify new classification method, RVRC to represent. And through experiments proved its feasibility.)

说明：  主要是基因识别的 matlab程序 一种全新的算法(Gene identification is mainly a new algorithm matlab program)

simulink应用实例，基于MATLAB6.5系列(Simulink application examples, based on a series of MATLAB6.5)

说明：  在用广义预测控制的基础上，加入了相位补偿，使得得到的结果更精确。(Based on generalized predictive control with on, joined the phase compensation, which makes the results more accurate.)

PLS部分最小二乘工具箱,包括广义互相关函数GCC时延估计，车牌识别定位程序的部分功能，均值便宜跟踪的示例，鲁棒性好，性能优越，使用大量的有限元法求解偏微分方程。(PLS PLS toolbox, Including the generalized cross-correlation function GCC time delay estimation, Part of the license plate recognition locator feature, Example tracking mean cheap, Robustness, superior performance, Using a large number of finite element method to solve partial differential equations.)

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.)

stochastic competition model