What people are saying - Write a review
We haven't found any reviews in the usual places.
ELEMENTS OF SYSTEM THEORY
Modelling Filtering and Identification
DISCRETE KALMAN FILTERING
4 other sections not shown
Other editions - View all
algorithm assumed asymptotically Ax(k Bu(k canonical correlation canonical variables CESL chapter closed loop Consider the following covariance matrix criterion Cx(k defined denotes deterministic input difference equation discrete distribution Du(k dynamic econometrics economic eigenvalues entropy error covariance ESTIMATED VALUES exogenous noise feature space model feature vector Fisher information matrix Gaussian given Hankel matrix hence initial condition x(0 input vector Kalman estimator Kalman filter least squares estimator linear Linear Regression LRF-model matrix which consists minimal ML-estimate model check null hypothesis observations optimal control output equation output vector parameter estimates parameter identifiability parameter vector positive definite prediction error estimation procedure process equation QUARTERS Fig random vector rank(H realization reconstructable recursive reduced form reference trajectory regression model rewritten Riccati equation self-tuning so-called solution stable stationary system theory theorem transformation uncorrelated unobservable variance vector x(k white noise y_(k z-transform zero