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(duì)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(duì)應(yīng)于變量數(shù)。且每一行提供次序——對(duì)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。
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.
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.
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.
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.
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
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(duì)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。
