Automatica (Journal of IFAC)
Application of color change feature in gastroscopic image retrieval
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Sensitivity shaping with degree constraint by nonlinear least-squares optimization
Automatica (Journal of IFAC)
Variance error, interpolation and experiment design
Automatica (Journal of IFAC)
Regularized spectrum estimation using stable spline kernels
Automatica (Journal of IFAC)
Hi-index | 754.84 |
We introduce a Kullback-Leibler (1968) -type distance between spectral density functions of stationary stochastic processes and solve the problem of optimal approximation of a given spectral density Ψ by one that is consistent with prescribed second-order statistics. In general, such statistics are expressed as the state covariance of a linear filter driven by a stochastic process whose spectral density is sought. In this context, we show (i) that there is a unique spectral density Φ which minimizes this Kullback-Leibler distance, (ii) that this optimal approximate is of the form Ψ/Q where the "correction term" Q is a rational spectral density function, and (iii) that the coefficients of Q can be obtained numerically by solving a suitable convex optimization problem. In the special case where Ψ = 1, the convex functional becomes quadratic and the solution is then specified by linear equations.