This paper reviews and evaluates suggested methods for estimating the time-delay of linear
systems in automatic control applications. A classification of the methods according to the …

… In this section we shall illustrate heuristically how a differential equation can be associated
with (l), and how it seems reasonable that asymptotic properties of (1) may be studied in …

The extended Kalman filter is an approximate filter for nonlinear systems, based on first-order
linearization. Its use for the joint parameter and state estimation problem for linear systems …

In this contribution we give an overview and discussion of the basic steps of System
Identification. The four main ingredients of the process that takes us from observed data to a …

… Here we have used q to denote the shift operator, so B(q) is a polynomial in 4-l. The … Steps
l-5 constitute our algorithm. Note that most of its computational burden is concentrated into the …

Two noniterative subspace-based algorithms which identify linear, time-invariant MIMO (multi-input/multioutput)
systems from frequency response data are presented. The algorithms …

… INTRODUCTION STOCHASTIC control theory has proved to be a useful tool for the design of
controllers for industrial processes[l]. In many practical applications it is, however, difficult to …

… For example, suppose that we know the probability density function (pdf) ℓ Y ( x , θ ) of an
observed random variable Y up to a parameter θ. Then the Fisher Information matrix for θ is(12…

… Unless otherwise stated, we will assume that all signals are quasi-stationary (Ljung, 1987).
This means that we can de"ne spectra and cross-spectra of signals, which will be denoted …

… We may call this difference the prediction error at time I for model L%(0), even though g.%
need not necessarily have been constructed as a "prediction" by the user as discussed above. …