mplab

用有限元法求解电磁场泊松方程 包括场方程和边界条件(Using the finite element method for solving the Poisson equation of electromagnetic fields, including the field equations and boundary conditions)

学习向量量化神经网络模型与学习算法及MATLAB实现(Learning vector quantization neural network model and learning algorithm)

数据挖掘中经典算法实现，可以节省不少时间哦(matlab code for classical algorithms in Data Mining)

张德丰matlab小波分析源代码，从信号时-频联合分析引入小波变换，将信号的多分辨率分析及Mallat算法作为全书的重点，并在此基础上，进一步阐述了双正交小波多分辨率分析、小波包多分辨率分析、提升小波应用，还讲述了小波分析在奇异性检测、去噪及数据压缩中的应用。(Zhang Defeng Matlab wavelet analysis of the source code from the signal- frequency joint analysis of the introduction of wavelet transform, multi-resolution analysis of the signal and Mallat algorithm as the focus of the book, and on this basis, further elaborated biorthogonal wavelet multi-resolution analysis wavelet packet multi-resolution analysis, the lifting wavelet applications, but also about the application of wavelet analysis in the singularity detection, de-noising and data compression.)

Creating 2D And 3d ploting App with Graphical User Interfaces in MATLAB. GUIs (also known as graphical user interfaces or UIs) provide point-and-click control of software applications, eliminating the need to learn a language or type commands in order to run the application. MATLAB apps are self-contained MATLAB programs with GUI front ends that automate a task or calculation. The GUI typically contains controls such as menus, toolbars, buttons, and sliders. Many MATLAB products, such as Curve Fitting Toolbox, Signal Processing Toolbox, and Control System Toolbox, include apps with custom user interfaces. You can also create your own custom apps, including their corresponding UIs, for others to use.

This demo nstrates 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. (This demo nstrates 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. )

自己下载整理的层次分析法材料，相信对你会有用的。自己下载看下吧(Download finishing their AHP materials, that will be useful to you. Facie it yourself downloads)

小波变换有关突变点的检测，通过离散小波变换与连续小波变换进行对比(Wavelet transform on the detection of mutations, through the discrete wavelet transform are compared with the continuous wavelet transform)

Here is a very simple version of PSO in Matlab. PSO is a very popular, population based metaheuristic algorithm that mimics swarming behavior/intelligence in order to solve optimization problems.

拉格朗日算法已知三组温度与压力数据:(3,53.5) (4,55.3) (5,56.2)求4.15对应的值为55.492375，结果正确。如果将以上数组顺序颠倒，已知压力55.492375求对应的温度，结果为4.185739，可以看到上下两个结果误差较大，就是说不能象这样去颠倒顺序去求值的。但是实际上我们可能经常要求知道压力去求温度，那么遇到这样的情况，只能采用试算法：一种简单的方法就是二分法迭代，书上都有介绍就不多讲了。(Lagrange method已知三组温度与压力数据:(3,53.5) (4,55.3) (5,56.2)求4.15对应的值为55.492375，结果正确。如果将以上数组顺序颠倒，已知压力55.492375求对应的温度，结果为4.185739，可以看到上下两个结果误差较大，就是说不能象这样去颠倒顺序去求值的。但是实际上我们可能经常要求知道压力去求温度，那么遇到这样的情况，只能采用试算法：一种简单的方法就是二分法迭代，书上都有介绍就不多讲了。)