Using Jacobi method and Gauss-Seidel iterative methods to solve the following system The Required precision is =0.00001, and the maximum iteration number N=25. Compare the number of iterations and the convergence of these two methods
標(biāo)簽： Gauss-Seidel iterative following methods
上傳時(shí)間： 2016-02-06
上傳用戶：zmy123
EBOOK: ARM SYSTEM DEVELOPER S GUIDE The ARM architecture is not a static constant but is being developed and improved to suit the applications Required by today’s consumer devices.
標(biāo)簽： architecture ARM DEVELOPER constant
上傳時(shí)間： 2016-02-20
上傳用戶：chongcongying
find the information about a host with the DNS retrieving system calls, such as gethostbyname() and gethostbyaddr(). (2) All the Required information are in the hostent structure. (3) All the aliases and IP addresses of the host is stored in the hostent structure using linked list (鏈表).
標(biāo)簽： gethostbyname information retrieving the
上傳時(shí)間： 2016-02-21
上傳用戶：13517191407
build a modbus client/server for use on the Protocessor (from FieldServer Technologies) Tools Required: 1. Microchip MCC18 compiler. 2. ICD2 debugger (or other device to program your PIC) 3. Protocessor hardware.
標(biāo)簽： Technologies Protocessor FieldServer modbus
上傳時(shí)間： 2016-02-27
上傳用戶：wfeel
Summary Many control applications require converting some analog input to a digital format. The ADCINC12 User Module is a general-purpose, 12-bit analog to digital converter (ADC) that does just that. This Application Note is meant to be a simple introduction into its operation. The steps Required to define, place, and write software are presented. Examples are developed in both assembly and C.
標(biāo)簽： applications converting Summary control
上傳時(shí)間： 2013-12-01
上傳用戶：WMC_geophy
File: fw.c Contents: Firmware frameworks task dispatcher and device request parser File: FX2.h Contents: EZ-USB FX2 constants, macros, datatypes, globals, and library function prototypes. File: FX2regs.h Contents: EZ-USB FX2 register declarations and bit mask definitions. File: periph.c Contents: Hooks Required to implement USB peripheral function. File: dscr.a51 Contents: This file contains descriptor data tables. File: dscr.a51 Contents: This file contains descriptor data tables. File: dscr.a51 Contents: This file contains descriptor data tables.
標(biāo)簽： File frameworks dispatcher Contents
上傳時(shí)間： 2014-01-18
上傳用戶：bakdesec
多項(xiàng)式曲線擬合 任意介數(shù) Purpose - Least-squares curve fit of arbitrary order working in C++ Builder 2007 as a template class, using vector<FloatType> parameters. Added a method to handle some EMathError exceptions. If do NOT want to use this just call PolyFit2 directly. usage: Call PolyFit by something like this. CPolyFit<double> PolyFitObj double correlation_coefficiant = PolyFitObj.PolyFit(X, Y, A) where X and Y are vectors of doubles which must have the same size and A is a vector of doubles which must be the same size as the number of coefficients Required. returns: The correlation coefficient or -1 on failure. produces: A vector (A) which holds the coefficients.
標(biāo)簽： Least-squares arbitrary Purpose Builder
上傳時(shí)間： 2013-12-18
上傳用戶：宋桃子
n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the Required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
標(biāo)簽： Rao-Blackwellised conditional filtering particle
上傳時(shí)間： 2013-12-17
上傳用戶：zhaiyanzhong
On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the Required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標(biāo)簽： demonstrates sequential Selection Bayesian
上傳時(shí)間： 2016-04-07
上傳用戶：lindor
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
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