-
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
標(biāo)簽：
parameters
AR-model
Routine
mar1psd
上傳時(shí)間：
2015-06-09
上傳用戶：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.
標(biāo)簽：
approximation
demonstrates
capabilities
Function
上傳時(shí)間：
2014-01-01
上傳用戶：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/.
標(biāo)簽：
univariate
and
bivariate
analysis
上傳時(shí)間：
2015-06-23
上傳用戶：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.
標(biāo)簽：
performance
asymptotic
examines
non-data
上傳時(shí)間：
2015-12-30
上傳用戶：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.
標(biāo)簽：
construction
separation
time-scale
particle
上傳時(shí)間：
2016-01-02
上傳用戶：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.
標(biāo)簽：
Probabilistic
Components
Principal
Analysis
上傳時(shí)間：
2016-04-28
上傳用戶：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
標(biāo)簽：
transmission
simulator
Channel
inputs
上傳時(shí)間：
2016-07-22
上傳用戶：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.
％
標(biāo)簽：
generalization
calculates
prediction
function
上傳時(shí)間：
2014-12-03
上傳用戶：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.
％
標(biāo)簽：
generalization
calculates
prediction
function
上傳時(shí)間：
2016-12-27
上傳用戶：腳趾頭
-
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.
標(biāo)簽：
mean-square
multiuser
receiver
project
上傳時(shí)間：
2014-11-21
上傳用戶：ywqaxiwang
