一個(gè)遺傳算法 這是一個(gè)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(shí)際上也不必查錯(cuò)。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(píng)價(jià)函數(shù)”即可。注意代碼 的設(shè)計(jì)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(gè)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(diǎn)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(tái)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(gè)變量提供上下界，第二行為第二個(gè)變量提供上下界，等等。
上傳時(shí)間： 2013-12-20
上傳用戶：myworkpost
EM算法是機(jī)器學(xué)習(xí)領(lǐng)域中常用的一種算法，這個(gè)文件是EM算法最簡單的一種實(shí)現(xiàn)，即在Gaussian Mixture model上面的EM。
標(biāo)簽： EM算法 機(jī)器學(xué)習(xí) 算法
上傳時(shí)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(shí)間： 2014-12-05
上傳用戶：410805624
Hybrid Monte Carlo sampling.SAMPLES = HMC(F, X, OPTIONS, GRADF) uses a hybrid Monte Carlo algorithm to sample from the distribution P ~ EXP(-F), where F is the first argument to HMC. The Markov chain starts at the point X, and the function GRADF is the gradient of the `energy function F.
標(biāo)簽： Carlo Monte algorithm sampling
上傳時(shí)間： 2013-12-02
上傳用戶：jkhjkh1982
Probability distribution functions. estimation - (dir) Probability distribution estimation. dsamp - Generates samples from discrete distribution. erfc2 - Normal cumulative distribution function. gmmsamp - Generates sample from Gaussian mixture model. gsamp - Generates sample from Gaussian distribution. cmeans - C-means (or K-means) clustering algorithm. mahalan - Computes Mahalanobis distance. pdfgauss - Computes probability for Gaussian distribution. pdfgmm - Computes probability for Gaussian mixture model. sigmoid - Evaluates sigmoid function.
標(biāo)簽： distribution Probability estimation functions
上傳時(shí)間： 2016-04-28
上傳用戶：13188549192
Image Compression A collection of simple routines for image compression using different techniques. 圖象壓縮的不同方法 BTCODE: Image compression Using Block Truncation Coding. PYRAMID: Image compression based on Gaussian Pyramids. DCTCOMPR: Image compression based on Discrete Cosine Transform. IMCOMPR: Image compression based on Singular Value Decomposition. The given codes can be also used in 2D noise suppression. Notes: The function "conv2fft" performs a 2D FFT-based convolution. Type "help conv2fft" on Matlab command window for more informations.
標(biāo)簽： Compression compression collection different
上傳時(shí)間： 2016-05-11
上傳用戶：磊子226
數(shù)值線性代數(shù)的Matlab應(yīng)用程序包 共13個(gè)程序函數(shù)，每個(gè)程序函數(shù)有相應(yīng)的例子函數(shù)一一對應(yīng)，以*Example.m命名 程序名稱 用途 Method 方法 GrmSch.m QR因子分解 classical Gram－Schmidt orthogonalization 格拉母－斯密特 MGrmSch.m QR因子分解 modified Gram－Schmidt iteration 修正格拉母－斯密特 householder.m QR因子分解 Householder 豪斯霍爾德QR因子分解 ZXEC.m 最小二乘擬合 polynomial interpolant 最小二乘插值多項(xiàng)式 NCLU.m LU因子分解 Gaussian elimination 不選主元素的高斯消元 PALU.m LU因子分解 partial pivoting Gaussian elimination 部分選主元的高斯消元 cholesky.m 楚因子分解 Cholesky Factorization 楚列斯基因子分解 PwItrt.m 求最大特征值 Power Iteration 冪迭代 Jacobi.m 求特征值 Jacobi iteration 按標(biāo)準(zhǔn)行方式次序的雅可比算法 Anld.m 求上Hessenberg Arnoldi Iteration 阿諾爾迪迭代 zuisu.m 解線性方程組 Steepest descent 最速下降法 CG.m 解線性方程組 Gradients 共軛梯度 BCG.m 解線性方程組 Biconjugate Gradients 雙共軛梯度
標(biāo)簽： Matlab 數(shù)值 應(yīng)用程序 函數(shù)
上傳時(shí)間： 2016-05-17
上傳用戶：小鵬
Sequential Monte Carlo without Likelihoods 粒子濾波不用似然函數(shù)的情況下 本文摘要:Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions in the presence of analytically or computationally intractable likelihood functions. Despite representing a substantial methodological advance, existing methods based on rejection sampling or Markov chain Monte Carlo can be highly inefficient, and accordingly require far more iterations than may be practical to implement. Here we propose a sequential Monte Carlo sampler that convincingly overcomes these inefficiencies. We demonstrate its implementation through an epidemiological study of the transmission rate of tuberculosis.
標(biāo)簽： Likelihoods Sequential Bayesian without
上傳時(shí)間： 2016-05-26
上傳用戶：離殤
Abstract-The effect of the companding process on QAM signals has been under investigation for the past several years. The compander, included in the PCM telephone network to improve voice performance, has an unusual affect on digital QAM data signals which are transmitted over the same channel. The quantization noise, generated by the companding process which is multiplicative (and asymmetric), degrades the detectability performance of the outermost points of the QAM constellation more than that of the inner points. The combined effect of the companding noise and the inherent white gaussian noise of the system, leads us to a re-examination of signal constellation design. In this paper we investigate the detectability performance of a number of candidates for signal constellations including, a typical rectangular QAM constellation, the same constellation with the addition of a smear-desmear operation, and two new improved QAM constellation designs with two-dimensional warpi
標(biāo)簽： investigation Abstract-The companding the
上傳時(shí)間： 2013-12-20
上傳用戶：英雄
這是一個(gè)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(shí)際上也不必查錯(cuò)。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(píng)價(jià)函數(shù)”即可。注意代碼 的設(shè)計(jì)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(gè)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(diǎn)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(tái)之間的高可移植性。
上傳時(shí)間： 2013-12-05
上傳用戶：lili123
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