mini norm算法Matlab代碼, DOA方向估計(jì)中最常用最簡(jiǎn)單的算法之一
標(biāo)簽： Matlab mini norm DOA
上傳時(shí)間： 2015-09-16
上傳用戶：李彥東
class for work with 3d Vectors: add, sub, saclar multiple, cross multiple, norm, div
標(biāo)簽： multiple Vectors saclar class
上傳時(shí)間： 2016-11-23
上傳用戶：stvnash
contains documents relating to improvement of adaptive beamforming using mixed norm algorithm, combination of lms with smi algorithm, sample code for implementation of lms in matlab
標(biāo)簽： beamforming improvement algorithm documents
上傳時(shí)間： 2014-01-21
上傳用戶：ommshaggar
Final draft of the ISO 14443 norm
標(biāo)簽： Final 14443 draft norm
上傳時(shí)間： 2013-12-18
上傳用戶：ynsnjs
基本矩陣運(yùn)算 : + - *, power, transpose, trace, determinant, minor, matrix of minor, cofactor, matrix of cofactor, adjoint, inverse, gauss, gaussjordan, linear transformation, LU decomposition , Gram-Schmidt process, similarity. b) Basic vectors functions : norm, distance, innerproduct,coldim, rowdim, rank, nullity. *
標(biāo)簽： matrix minor determinant transpose
上傳時(shí)間： 2013-12-09
上傳用戶：541657925
模擬人物和動(dòng)畫的書籍，由著名圖形學(xué)專家norm BADLER 編寫，全面地介紹了人物動(dòng)畫的生成和開(kāi)發(fā)。
上傳時(shí)間： 2016-01-15
上傳用戶：康郎
數(shù)值分析算法源碼(java) 這個(gè)學(xué)期一邊學(xué)習(xí)java一邊學(xué)習(xí)數(shù)值分析,因此用java寫了一個(gè)數(shù)值分析算法的軟件包numericalAnalysis. [說(shuō)明] 適合使用者:會(huì)java的,想要學(xué)習(xí)數(shù)值分析算法的人. 本代碼對(duì)照書:數(shù)值分析第二版,史萬(wàn)明等編,北京理工大學(xué)出版社. 本代碼盡量按書中描述的來(lái)寫,可以提供參考. [使用方法] 在java的ide中新建一個(gè)項(xiàng)目,把numericalAnalysis包直接拷貝到此項(xiàng)目的源文件夾中,然后要解決什么問(wèn)題,就相應(yīng)的編譯運(yùn)行什么包. 另外有這些類的API提供參考,可以自己根據(jù)自己要求寫驅(qū)動(dòng)類.運(yùn)行API文件夾中的index.html文件就行. [包的結(jié)構(gòu)] numericalAnalysis包中又含有9個(gè)包,除function包的是接口外,其余包都含有一個(gè)獨(dú)立的數(shù)值分析問(wèn)題的類以及其驅(qū)動(dòng)類.9個(gè)包如下: differential:微分問(wèn)題 equation:方程 function:只含一個(gè)接口,用來(lái)讓用戶寫自己的函數(shù) functionApproximation:離散情況下函數(shù)逼近問(wèn)題 integration:積分問(wèn)題 interpolation:插值問(wèn)題 linearEquationGroup:線性方程組問(wèn)題(包括過(guò)定方程組) norm:求解向量和矩陣的范數(shù) ode:常微分方程數(shù)值解的求解
標(biāo)簽： java numericalAnalysis 數(shù)值分析 算法
上傳時(shí)間： 2014-01-04
上傳用戶：wff
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
標(biāo)簽： identification considered features separati
上傳時(shí)間： 2016-09-20
上傳用戶：FreeSky
The toolbox solves a variety of approximate modeling problems for linear static models. The model can be parameterized in kernel, image, or input/output form and the approximation criterion, called misfit, is a weighted norm between the given data and data that is consistent with the model. There are three main classes of functions in the toolbox: transformation functions, misfit computation functions, and approximation functions. The approximation functions derive an approximate model from data, the misfit computation functions are used for validation and comparison of models, and the transformation functions are used for deriving one model representation from another. KEYWORDS: Total least squares, generalized total least squares, software implementation.
標(biāo)簽： approximate The modeling problems
上傳時(shí)間： 2013-12-20
上傳用戶：15071087253
Mapack可用來(lái)做矩陣運(yùn)算 Mapack is a .NET class library for basic linear algebra computations. It supports the following matrix operations and properties: Multiplication, Addition, Subtraction, Determinant, norm1, norm2, Frobenius norm, Infinity norm, Rank, Condition, Trace, Cholesky, LU, QR, Single Value decomposition, Least Squares solver, Eigenproblem solver, Equation System solver. The algorithms were adapted from Mapack for COM, Lapack and the Java Matrix Package.
標(biāo)簽： Mapack computations supports algebra
上傳時(shí)間： 2017-01-26
上傳用戶：tb_6877751
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