Routine mar1psd: To compute the power spectum by AR-model parameters. Input parameters: ip : AR model order (integer) ep : White noise VARIANCE of model input (real) ts : Sample interval in seconds (real) a : Complex array of AR parameters a(0) to a(ip) Output parameters: psdr : Real array of power spectral density values psdi : Real work array in chapter 12
标签： parameters AR-model Routine mar1psd
上传时间： 2015-06-09
上传用户：playboys0
This program demonstrates some function approximation capabilities of a Radial Basis Function Network. The user supplies a set of training points which represent some "sample" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the VARIANCE for the network. Using the training samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to approximate the function between the given "sample" points.
标签： approximation demonstrates capabilities Function
上传时间： 2014-01-01
上传用户：zjf3110
A series of .c and .m files which allow one to perform univariate and bivariate wavelet analysis of discrete time series. Noother wavelet package is necessary -- everything is contained in this archive. The C-code computes the DWT and maximal overlap DWT. MATLAB routines are then used to compute such quantities as the wavelet VARIANCE, coVARIANCE, correlation, cross-coVARIANCE and cross-correlation. Approximate confidence intervals are available for all quantities except the cross-coVARIANCE and cross-correlation. A set of commands is provided. For a description of this example, please see http://www.eurandom.tue.nl/whitcher/software/.
标签： univariate and bivariate analysis
上传时间： 2015-06-23
上传用户：chongcongying
This paper examines the asymptotic (large sample) performance of a family of non-data aided feedforward (NDA FF) nonlinear least-squares (NLS) type carrier frequency estimators for burst-mode phase shift keying (PSK) modulations transmitted through AWGN and flat Ricean-fading channels. The asymptotic performance of these estimators is established in closed-form expression and compared with the modified Cram`er-Rao bound (MCRB). A best linear unbiased estimator (BLUE), which exhibits the lowest asymptotic VARIANCE within the family of NDA FF NLS-type estimators, is also proposed.
标签： performance asymptotic examines non-data
上传时间： 2015-12-30
上传用户：225588
We present a particle filter construction for a system that exhibits time-scale separation. The separation of time-scales allows two simplifications that we exploit: i) The use of the averaging principle for the dimensional reduction of the system needed to solve for each particle and ii) the factorization of the transition probability which allows the Rao-Blackwellization of the filtering step. Both simplifications can be implemented using the coarse projective integration framework. The resulting particle filter is faster and has smaller VARIANCE than the particle filter based on the original system. The convergence of the new particle filter to the analytical filter for the original system is proved and some numerical results are provided.
标签： construction separation time-scale particle
上传时间： 2016-01-02
上传用户：fhzm5658
Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal ％ component subspace U of dimension PPCA_DIM using a centred coVARIANCE matrix X. The variable VAR contains the off-subspace VARIANCE (which is assumed to be spherical), while the vector LAMBDA contains the VARIANCEs of each of the principal components. This is computed using the eigenvalue and eigenvector decomposition of X.
标签： Probabilistic Components Principal Analysis
上传时间： 2016-04-28
上传用户：qb1993225
ofdm信道特性 Channel transmission simulator Channel transmission simulator ％ ％ inputs: ％ sig2 - noise VARIANCE ％ Mt - number of Tx antennas ％ Mr - number of Rx antennas ％ x - vector of complex input symbols (for MIMO, this is a matrix, where each column ％ is the value of the antenna outputs at a single time instance) ％ H - frequency selective channel - represented in block-Toeplitz form for MIMO transmission ％ N - number of symbols transmitted in OFDM frame ％ ％ outputs: ％ y - vector of channel outputs (matrix for MIMO again, just like x matrix) ％ create noise vector sequence (each row is a different antenna, each column is a ％ different time index) note: noise is spatially and temporally white
标签： transmission simulator Channel inputs
上传时间： 2016-07-22
上传用户：kelimu
This function calculates Akaike s final prediction error ％ estimate of the average generalization error. ％ ％ [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the ％ final prediction error estimate (fpe), the effective number of ％ weights in the network if the network has been trained with ％ weight decay, an estimate of the noise VARIANCE, and the Gauss-Newton ％ Hessian. ％
标签： generalization calculates prediction function
上传时间： 2014-12-03
上传用户：maizezhen
This function calculates Akaike s final prediction error ％ estimate of the average generalization error for network ％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive ％ counterparts. ％ ％ [FPE,deff,varest,H] = nnfpe(method,NetDef,W1,W2,U,Y,NN,trparms,skip,Chat) ％ produces the final prediction error estimate (fpe), the effective number ％ of weights in the network if it has been trained with weight decay, ％ an estimate of the noise VARIANCE, and the Gauss-Newton Hessian. ％
标签： generalization calculates prediction function
上传时间： 2016-12-27
上传用户：脚趾头
In this project we analyze and design the minimum mean-square error (MMSE) multiuser receiver for uniformly quantized synchronous code division multiple access (CDMA) signals in additive white Gaussian noise (AWGN) channels.This project is mainly based on the representation of uniform quantizer by gain plus additive noise model. Based on this model, we derive the weight vector and the output signal-to-interference ratio (SIR) of the MMSE receiver. The effects of quantization on the MMSE receiver performance is characterized in a single parameter named 鈥漞quivalent noise VARIANCE鈥? The optimal quantizer stepsize which maximizes the MMSE receiver output SNR is also determined.
标签： mean-square multiuser receiver project
上传时间： 2014-11-21
上传用户：ywqaxiwang
