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Gaussian

Gaussian是一個功能強大的量子化學綜合軟件包。其可執行程序可在不同型號的大型計算機，超級計算機，工作站和個人計算機上運行，并相應有不同的版本。高斯功能：過渡態能量和結構、鍵和反應能量、分子軌道、原子電荷和電勢、振動頻率、紅外和拉曼光譜、核磁性質、極化率和超極化率、熱力學性質、反應路徑，計算可以對體系的基態或激發態執行。可以預測周期體系的能量，結構和分子軌道。因此，Gaussian可以作為功能強大的工具，用于研究許多化學領域的課題，例如取代基的影響，化學反應機理，勢能曲面和激發能等等。常常與gaussview連用。

zemax源碼： This DLL models a standard ZEMAX surface type, either plane, sphere, or conic The surfac

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對應的生成矩陣G

利用二元域的高斯消元法得到輸入矩陣H對應的生成矩陣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

標簽： 矩陣二元高斯輸入

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

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。對一特定的應用修正此代碼，用戶只需改變常數的定義并且定義“評價函數”即可。注意代碼的設計是求最大值，其中的目標函數只能取正值；且函數值和個體的適應值之間沒有區別。該系統使用比率選擇、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。要求輸入的文件應該命名為‘gadata.txt’；系統產生的輸出文件為‘galog.txt’。輸入的文件由幾行組成：數目對應于變量數。且每一行提供次序——對應于變量的上下界。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。

標簽： 算法源代碼

上傳時間： 2015-10-16

上傳用戶：曹云鵬
基于libsvm

基于libsvm，開發的支持向量機圖形界面(初級水平)應用程序，并提供了關于C和sigma的新的參數選擇方法，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 distribu

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, industria

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.

標簽： monitoring sequential industria accurate

上傳時間： 2013-12-17

上傳用戶：familiarsmile
用于產生gamma分布的噪聲序列

用于產生gamma分布的噪聲序列，以及分析Gaussian噪聲的各參數。

標簽： gamma 分布序列

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

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。對一特定的應用修正此代碼，用戶只需改變常數的定義并且定義“評價函數”即可。注意代碼的設計是求最大值，其中的目標函數只能取正值；且函數值和個體的適應值之間沒有區別。該系統使用比率選擇、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。要求輸入的文件應該命名為‘gadata.txt’；系統產生的輸出文件為‘galog.txt’。輸入的文件由幾行組成：數目對應于變量數。且每一行提供次序——對應于變量的上下界。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。

標簽： 算法源代碼

上傳時間： 2013-12-20

上傳用戶：myworkpost
EM算法是機器學習領域中常用的一種算法

EM算法是機器學習領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，即在Gaussian Mixture model上面的EM。

標簽： EM算法機器學習算法

上傳時間： 2013-12-11

上傳用戶：wxhwjf
The software implements particle filtering and Rao Blackwellised particle filtering for conditionall

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.

標簽： filtering particle Blackwellised conditionall

上傳時間： 2014-12-05

上傳用戶：410805624

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