三維矢量有限元-矩量法電磁場分析程序。 EMAP5 is a full-wave electromagnetic field solver that combines the method of moments (MOM) with a vector finite element method (VFEM). It employs the finite element method (FEM) to analyze a dielectric volume, and employs the method of moments (MoM) to solve for currents on the surface of (or external to) this volume. The two methods are coupled through the fields on the dielectric surface.
標簽： electromagnetic full-wave combines solver
上傳時間： 2016-04-03
上傳用戶：cylnpy
多項式曲線擬合 任意介數 Purpose - Least-squares curve fit of arbitrary order working in C++ Builder 2007 as a template class, using vector<FloatType> parameters. Added a method to handle some EMathError exceptions. If do NOT want to use this just call PolyFit2 directly. usage: Call PolyFit by something like this. CPolyFit<double> PolyFitObj double correlation_coefficiant = PolyFitObj.PolyFit(X, Y, A) where X and Y are vectors of doubles which must have the same size and A is a vector of doubles which must be the same size as the number of coefficients required. returns: The correlation coefficient or -1 on failure. produces: A vector (A) which holds the coefficients.
標簽： Least-squares arbitrary Purpose Builder
上傳時間： 2013-12-18
上傳用戶：宋桃子
This string-include defines all string functions as inline functions. Use gcc. It also assumes ds=es=data space, this should be normal. Most of the string-functions are rather heavily hand-optimized, see especially strtok,strstr,str[c]spn. They should work, but are not very easy to understand. Everything is done entirely within the register set, making the functions fast and clean.
標簽： functions string-include defines assumes
上傳時間： 2014-01-09
上傳用戶：tedo811
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
標簽： Rauch-Tung-Striebel algorithm smoother which
上傳時間： 2016-04-15
上傳用戶：zhenyushaw
很經典的空時編碼書籍，英文版。 "Space-Time Coding Theory and Practice" writen by Hamid Jafarkhani Cambridge Pre
上傳時間： 2016-04-19
上傳用戶：onewq
This Two-Category Classifier Using Discriminant Functions to separeate two classes. The Classifier is designed on classes which has two feature vectors and other case it has one feature vector.
標簽： Classifier Discriminant Two-Category Functions
上傳時間： 2016-04-23
上傳用戶：2525775
描述工廠管理多個工人 工廠類 屬性：工廠名稱 行為：添加 刪除工人 工人類 屬性：姓名 （1）使用數組裝載工人對象，這里注意當數組滿時添加工人應做一些什么樣的處理。 （2）將數組改為Vector類裝載工人對象。
上傳時間： 2016-04-25
上傳用戶：gaojiao1999
Creates a Gaussian mixture model with specified architecture.MIX = GMM(DIM, NCENTRES, COVARTYPE) takes the dimension of the space DIM, the number of centres in the mixture model and the type of the mixture model, and returns a data structure MIX.
標簽： architecture COVARTYPE specified Gaussian
上傳時間： 2016-04-28
上傳用戶：dyctj
KMEANS Trains a k means cluster model.CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means algorithm to set the centres of a cluster model. The matrix DATA represents the data which is being clustered, with each row corresponding to a vector. The sum of squares error function is used. The point at which a local minimum is achieved is returned as CENTRES.
標簽： CENTRES KMEANS OPTIONS cluster
上傳時間： 2014-01-07
上傳用戶：zhouli
Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal ％ component subspace U of dimension PPCA_DIM using a centred covariance matrix X. The variable VAR contains the off-subspace variance (which is assumed to be spherical), while the vector LAMBDA contains the variances of each of the principal components. This is computed using the eigenvalue and eigenvector decomposition of X.
標簽： Probabilistic Components Principal Analysis
上傳時間： 2016-04-28
上傳用戶：qb1993225
