A dissipative particle swarm optimization is developed according to the self-organization of dissipative Structure. The negative entropy is introduced to construct an opening dissipative system that is far-from-equilibrium so as to driving the irreversible evolution process with better fitness. The testing of two multimodal functions indicates it improves the performance effectively. Structure. The negative entropy is introduced to construct an opening dissipative system that is far-from-equilibrium so as to driving the irreversible evolution process with better fitness. The testing of two multimodal functions indicates it improves the performance effectively.
標(biāo)簽： self-organization optimization dissipative developed
上傳時間： 2016-03-31
上傳用戶：zgu489
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
上傳時間： 2013-12-17
上傳用戶：zhaiyanzhong
The IA-32 Intel Architecture Software Developer’s Manual, Volume 2: Instruction Set Reference (Order Number 245471) is part of a three-volume set that describes the architecture and programming environment of all IA-32 Intel® Architecture processors. the IA-32 Intel Architecture Software Developer’s Manual, Volume 2, describes the instructions set of the processor and the opcode Structure. These two volumes are aimed at application programmers who are writing programs to run under existing operating systems or executives.
標(biāo)簽： Architecture Instruction Developer Reference
上傳時間： 2013-12-15
上傳用戶：xsnjzljj
Noncoherent receivers are attractive for pulsed UWB systems due to the implementation simplicity. To alleviate the noise effect in detecting UWB PPM signals, this letter proposes a simple yet flexible weighted noncoherent receiver Structure, which adopts a square-law integrator multiplied with a window function.
標(biāo)簽： implementation Noncoherent attractive simplicity
上傳時間： 2013-12-01
上傳用戶：wys0120
In 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
上傳時間： 2013-12-14
上傳用戶：小儒尼尼奧
Creates a Gaussian mixture model with specified architecture.MIX = GMM(DIM, NCENTRES, COVARTYPE) takes the dimension of the space DIM, the number of centres in the mixture model and the type of the mixture model, and returns a data Structure MIX.
標(biāo)簽： architecture COVARTYPE specified Gaussian
上傳時間： 2016-04-28
上傳用戶：dyctj
his paper discuss how to design data acquisition and process system based on USB Transmitting. We further introduce some system’s Structure such as Operation
標(biāo)簽： Transmitting acquisition discuss process
上傳時間： 2013-12-26
上傳用戶：qoovoop
I made a lot of changed on this object,such as * // 1.Encapsulates all code in one userobjet,since PB does not * // support "Address of Function" , so we can not set new * // WndProc, just makes the object more easy to use. * // 2.Uses Structure array instead of Datastore * // 3.Calc width of menuitem at runtime(MEASUREITEM) * // 4.Draw disabled status
標(biāo)簽： Encapsulates userobjet changed object
上傳時間： 2014-01-14
上傳用戶：lx9076
％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - Structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％
標(biāo)簽： multidimensional estimation algorithm Gaussian
上傳時間： 2013-12-03
上傳用戶：我們的船長
This demonstration illustrates the application of adaptive filters to signal separation using a Structure called an adaptive line enhancer (ALE). In adaptive line enhancement, a measured signal x(n) contains two signals, an unknown signal of interest v(n), and a nearly-periodic noise signal eta(n). The goal is to remove the noise signal from the measured signal to obtain the signal of interest.
標(biāo)簽： demonstration application illustrates separation
上傳時間： 2014-09-08
上傳用戶：2525775
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