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Knowledge of the process noise covariance matrix is essential for the application of Kalman filtering. However, it is usually a difficult task to obtain an explicit expression of for large time varying systems. This paper looks at an adaptive Kalman filter method for dynamic harmonic state estimation and harmonic injection tracking.
標簽： application covariance Knowledge essential
上傳時間： 2014-01-19
上傳用戶：litianchu
function y_cum = cum2x (x,y, maxlag, nsamp, overlap, flag) ％CUM2X Cross-covariance ％ y_cum = cum2x (x,y,maxlag, samp_seg, overlap, flag) ％ x,y - data vectors/matrices with identical dimensions ％ if x,y are matrices, rather than vectors, columns are ％ assumed to correspond to independent realizations, ％ overlap is set to 0, and samp_seg to the row dimension. ％ maxlag - maximum lag to be computed [default = 0] ％ samp_seg - samples per segment [default = data_length] ％ overlap - percentage overlap of segments [default = 0] ％ overlap is clipped to the allowed range of [0,99].
標簽： cum2x y_cum Cross-covariance function
上傳時間： 2015-09-08
上傳用戶：xieguodong1234
又一種增量人臉學習算法——參考文獻“Candid covariance-Free Incremental Principal Component Analysis.”
標簽： covariance-Free Incremental Component Principal
上傳時間： 2016-08-08
上傳用戶：tyler
Fast DOA Estimation Algorithm using Pseudo covariance Matrix的程序
標簽： covariance Estimation Algorithm Pseudo
上傳時間： 2017-07-18
上傳用戶：wqxstar
This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
標簽： instantaneous algorithm Bayesian Gaussian
上傳時間： 2013-12-19
上傳用戶：jjj0202
尋找函數的全局極小值，global minimization of contrast function with random restarts the data are assumed whitened (i.e. with identity covariance matrix). The output is such that Wopt*x are the independent sources.
上傳時間： 2013-12-15
上傳用戶：康郎
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/.
標簽： univariate and bivariate analysis
上傳時間： 2015-06-23
上傳用戶：chongcongying
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
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.
標簽： Probabilistic Components Principal Analysis
上傳時間： 2016-04-28
上傳用戶：qb1993225
EKF-SLAM Simulator This version of the simulator uses global variables for all large objects, such as the state covariance matrix. While bad programming practice, it is a necessary evil for MatLab efficiency, as MatLab has no facility to avoid gratuitous memory allocation and copying when passing (and modifying) variables between functions. With this concession, effort has been made to keep the code as clean and modular as possible.
標簽： Simulator simulator variables EKF-SLAM
上傳時間： 2016-05-02
上傳用戶：lunshaomo
