zemax源碼： This DLL models a standard ZEMAX surface type, either plane, sphere, or conic The surface also demonstrates a user-defined apodization filter The filter is defined as part of the real ray trace, case 5 The filter can be used at the stop to produce x-y Gaussian apodization similar to the Gaussian pupil apodization in ZEMAX but separate in x and y. The amplitude apodization is of the form EXP[-(Gx(x/R)^2 + Gy(y/R)^2)] The transmission is of the form EXP[-2(Gx(x/R)^2 + Gy(y/R)^2)] where x^2 + y^2 = r^2 R = semi-diameter The tranmitted intensity is maximum in the center. T is set to 0 if semi-diameter < 1e-10 to avoid division by zero.
標(biāo)簽： standard surface models either
上傳時間： 2013-12-05
上傳用戶：003030
利用二元域的高斯消元法得到輸入矩陣H對應(yīng)的生成矩陣G，同時返回與G滿足mod(G*P ,2)=0的矩陣P，其中P 表示P的轉(zhuǎn)置 使用方法：[P，G]=Gaussian(H,x)，x=1 or 2,1表示G的左邊為單位陣
上傳時間： 2014-11-27
上傳用戶：semi1981
這是一個非常簡單的遺傳算法源代碼，代碼保證盡可能少，實際上也不必查錯。對一特定的應(yīng)用修正此代碼，用戶只需改變常數(shù)的定義并且定義“評價函數(shù)”即可。注意代碼 的設(shè)計是求最大值，其中的目標(biāo)函數(shù)只能取正值；且函數(shù)值和個體的適應(yīng)值之間沒有區(qū)別。該系統(tǒng)使用比率選擇、精華模型、單點雜交和均勻變異。如果用 Gaussian變異替換均勻變異，可能得到更好的效果。代碼沒有任何圖形，甚至也沒有屏幕輸出，主要是保證在平臺之間的高可移植性。讀者可以從ftp.uncc.edu, 目錄 coe/evol中的文件prog.c中獲得。要求輸入的文件應(yīng)該命名為‘gadata.txt’；系統(tǒng)產(chǎn)生的輸出文件為‘galog.txt’。輸入的 文件由幾行組成：數(shù)目對應(yīng)于變量數(shù)。且每一行提供次序——對應(yīng)于變量的上下界。如第一行為第一個變量提供上下界，第二行為第二個變量提供上下界，等等。
上傳時間： 2015-10-16
上傳用戶：曹云鵬
基于libsvm，開發(fā)的支持向量機圖形界面(初級水平)應(yīng)用程序，并提供了關(guān)于C和sigma的新的參數(shù)選擇方法，使得SVM的使用更加簡單直觀.參考文章 Fast and Efficient Strategies for Model Selection of Gaussian Support Vector Machine 可google之。
標(biāo)簽： libsvm
上傳時間： 2015-10-16
上傳用戶：cuibaigao
In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
標(biāo)簽： sequential simulation posterior overview
上傳時間： 2015-12-31
上傳用戶：225588
The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial and nancial problems. Although the Kalman lter is effective in the linear-Gaussian case, new methods of dealing with sequential data are required with non-standard models. Recently, there has been renewed interest in simulation-based techniques. The basic idea behind these techniques is that the current state of knowledge is encapsulated in a representative sample from the appropriate posterior distribution. As time goes on, the sample evolves and adapts recursively in accordance with newly acquired data. We give a critical review of recent developments, by reference to oil well monitoring, ion channel monitoring and tracking problems, and propose some alternative algorithms that avoid the weaknesses of the current methods.
標(biāo)簽： monitoring sequential industria accurate
上傳時間： 2013-12-17
上傳用戶：familiarsmile
用于產(chǎn)生gamma分布的噪聲序列，以及分析Gaussian噪聲的各參數(shù)。
上傳時間： 2016-01-08
上傳用戶：xfbs821
一個遺傳算法 這是一個非常簡單的遺傳算法源代碼，是由Denis Cormier (North Carolina State University)開發(fā)的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代碼保證盡可能少，實際上也不必查錯。對一特定的應(yīng)用修正此代碼，用戶只需改變常數(shù)的定義并且定義“評價函數(shù)”即可。注意代碼 的設(shè)計是求最大值，其中的目標(biāo)函數(shù)只能取正值；且函數(shù)值和個體的適應(yīng)值之間沒有區(qū)別。該系統(tǒng)使用比率選擇、精華模型、單點雜交和均勻變異。如果用 Gaussian變異替換均勻變異，可能得到更好的效果。代碼沒有任何圖形，甚至也沒有屏幕輸出，主要是保證在平臺之間的高可移植性。讀者可以從ftp.uncc.edu, 目錄 coe/evol中的文件prog.c中獲得。要求輸入的文件應(yīng)該命名為‘gadata.txt’；系統(tǒng)產(chǎn)生的輸出文件為‘galog.txt’。輸入的 文件由幾行組成：數(shù)目對應(yīng)于變量數(shù)。且每一行提供次序——對應(yīng)于變量的上下界。如第一行為第一個變量提供上下界，第二行為第二個變量提供上下界，等等。
上傳時間： 2013-12-20
上傳用戶：myworkpost
EM算法是機器學(xué)習(xí)領(lǐng)域中常用的一種算法，這個文件是EM算法最簡單的一種實現(xiàn)，即在Gaussian Mixture model上面的EM。
標(biāo)簽： EM算法 機器學(xué)習(xí) 算法
上傳時間： 2013-12-11
上傳用戶：wxhwjf
The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downloading the file, type "tar -xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab and run the demo.
標(biāo)簽： filtering particle Blackwellised conditionall
上傳時間： 2014-12-05
上傳用戶：410805624
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