DBSCAN是一個基于密度的聚類算法。改算法將具有足夠高度的區(qū)域劃分為簇，并可以在帶有“噪聲”的空間數(shù)據(jù)庫中發(fā)現(xiàn)任意形狀的聚類。-DBSCAN is a density-based clustering algorithm. Algorithm change will have enough height to the regional cluster. and to be with the "noise" of the spatial database found clusters of arbitrary shape.
上傳時間： 2013-12-28
上傳用戶：q123321
EM算法是機器學習領(lǐng)域中常用的一種算法，這個文件是EM算法最簡單的一種實現(xiàn)，即在Gaussian Mixture model上面的EM。
上傳時間： 2013-12-11
上傳用戶：wxhwjf
偽隨機數(shù)生成器，Implementation of the Quasi-Random Number generator currently hardwired to no more than 52 dimensions
上傳時間： 2013-12-20
上傳用戶：teddysha
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.
標簽： implementation Noncoherent attractive simplicity
上傳時間： 2013-12-01
上傳用戶：wys0120
A new PLL topology and a new simplified linear model are presented. The new fractional-N synthesizer presents no reference spurs and lowers the overall phase noise, thanks to the presence of a SampleJHold block. With a new simulation methodology it is possible to perform very accurate simulations, whose results match closely those obtained with the linear PLL model developed.
標簽： new fractional-N synthesizer simplified
上傳時間： 2016-04-14
上傳用戶：hjshhyy
This paper presents the key circuits of a 1MHz bandwidth, 750kb/s GMSK transmitter. The fractional-N synthesizer forming the basis of the transmitter uses a combined phasefrequency detector (PFD) and digital-to-analog converter (DAC) circuit element to obtain >28dB high frequency noise reduction when compared to classicalfrequency synthesis.
標簽： fractional-N transmitter bandwidth circuits
上傳時間： 2016-04-14
上傳用戶：er1219
Fast settling-time added to the already conflicting requirements of narrow channel spacing and low phase noise lead to Fractional4 divider techniques for PLL synthesizers. We analyze discrete "beat-note spurious levels from arbitrary modulus divide sequences including those from classic accumulator methods.
標簽： settling-time requirements conflicting already
上傳時間： 2016-04-14
上傳用戶：liansi
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.
標簽： filtering particle Blackwellised conditionall
上傳時間： 2014-12-05
上傳用戶：410805624
This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". 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： reversible algorithm the nstrates
上傳時間： 2014-01-08
上傳用戶：cuibaigao
runs Kalman-Bucy filter over observations matrix Z for 1-step prediction onto matrix X (X can = Z) with model order p V = initial covariance of observation sequence noise returns model parameter estimation sequence A, sequence of predicted outcomes y_pred and error matrix Ey (reshaped) for y and Ea for a along with inovation prob P = P(y_t | D_t-1) = evidence
標簽： matrix observations Kalman-Bucy prediction
上傳時間： 2016-04-28
上傳用戶：huannan88
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