UWB 功率控制 容量 Main Matlab script is in runsim.m. It generates random topologies, optimizes, and display results. IMPORTANT: you may need to add manually the lib path in Matlab in order to get all the necessary functions. Reference: Radunovic, Le Boudec, "Joint Power Control, Scheduling and Routing in UWB NETWORKS"
標簽： topologies generates optimizes Matlab
上傳時間： 2015-08-14
上傳用戶：shanml
there are some newly released Neural Network Example Programs for Character Recognition, which based on Image Processing Toolbox,Neural Network Toolbox in MATLAB, which is quite informative for the beginners in Neural NETWORKS applicators
標簽： Recognition Character Programs released
上傳時間： 2015-09-17
上傳用戶：CHINA526
一份計算機網(wǎng)絡(luò)報告2，含一些常用網(wǎng)絡(luò)命令，A report of two computer NETWORKS，Containing some of the commonly used network order.
標簽： 計算機網(wǎng)絡(luò) 報告
上傳時間： 2014-01-04
上傳用戶：lunshaomo
The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural NETWORKS featuring different adaptation algorithms.
標簽： Neural collection implement Adaptive
上傳時間： 2015-12-01
上傳用戶：181992417
Abstract—Mobile devices performing video coding and streaming over wireless and pervasive communication NETWORKS are limited in energy supply. To prolong the operational lifetime of these devices, an embedded video encoding system should be able to adjust its computational complexity and energy consumption as demanded by the situation and its environment.
標簽： performing and communica streaming
上傳時間： 2014-01-21
上傳用戶：pompey
bp 神經(jīng)網(wǎng)絡(luò) ，解決異或問題 NETWORKS vc++6.0
上傳時間： 2014-06-28
上傳用戶：chenlong
Sector is a system infrastructure software that provides functionality for distributed data storage, access, and analysis/processing. It automatically manages large volumetric data across servers or clusters, even those over distributed wide area high speed NETWORKS. Sector provides simple tools and APIs to access and/or process the data. Data and server locations are transparent to users, as the whole Sector network is a single networked super computer to the users.
標簽： infrastructure functionality distributed software
上傳時間： 2013-12-21
上傳用戶：極客
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.
標簽： Rao-Blackwellised conditional filtering particle
上傳時間： 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.
標簽： demonstrates sequential Selection Bayesian
上傳時間： 2016-04-07
上傳用戶：lindor
模式識別學(xué)習(xí)綜述.該論文的英文參考文獻為303篇.很有可讀價值.Abstract— Classical and recent results in statistical pattern recognition and learning theory are reviewed in a two-class pattern classification setting. This basic model best illustrates intuition and analysis techniques while still containing the essential features and serving as a prototype for many applications. Topics discussed include nearest neighbor, kernel, and histogram methods, Vapnik–Chervonenkis theory, and neural NETWORKS. The presentation and the large (thogh nonexhaustive) list of references is geared to provide a useful overview of this field for both specialists and nonspecialists.
標簽： statistical Classical Abstract pattern
上傳時間： 2013-11-25
上傳用戶：www240697738
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