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Based on the notions of template functions a multitude of system parameter estimation methods is presented as a coherent picture. This leads to increased insight and to new, practical estimation schemes, adaptable for a wide variety of situations.

It is well known that the (naive) least squares approach to the estimation problem in functional relationships leads to inconsistent results if the information matrix is not known exactly.

In this paper the problem of input signal design for system identification is considered. The input signal is amplitude-constrained and the case of a finite-length Input sequence Is Investigated. Various optiraality criteria are used to derive the optimal identifying input sequence. In particular: Rissanen's Minimum Description length (MHL) criterion, the determinant of the information matrix, the information matrix determinant ratio, and the trace of the parameter error covariance matrix are considered as the criteria for optimality of identification. These input design criteria can also be used in structure selection (multivariate systems) or in model order (univariate systems) estimation procedures. The emphasis is pur on autoregressive models, and the input signal sequences are derived using a dynamic programming approach. A comparative study based on analytical results as well as on simulations is presented.

Forced by university curriculum reform (5-→ 4-year curriculum duration) the introductory control engineering course has been shifted from the 3rd year to the 2nd year and the total teaching program has been reconstructed. Therefore the idea originated to concentrate the laboratory experiments around one central process, on which sequentially various aspects can be studied, such as measurements, modelling, identification, stochastic characteristics, analog control, digital control, etc.

Using cross-validation ideas, two procedures are proposed for making a choice between different model structures used for (approximate) modelling of multivariable systems. The procedures are derived under fairly general conditions: the ‘true’ system does not need to be contained in the model set; model structures do not need to be nested and different criteria may be used for model estimation and validation. The proposed structure selection rules are invariant to parameter scaling. Under certain conditions (essentially requiring that the system belongs to the model set and that the maximum likelihood method is used for parameter estimation) they are shown to be asymptotically equivalent to the (generalized) Akaike structure selection criteria

An account is given of useful and effective applications of information theory in identification and some new problems are indicated, which can be solved within an information-theoretic framework. In the first part of the paper a brief survey is presented of information measures and their mutual relations. Restriction has been made to probabilistic information measures. Non-probabilistic measures did not find any application in identification so far.

A method is presented for the use of a unit impulse response and responses to impulse pairs of variable separation in the calculation of the second-degree kernels of a quadratic system. A quadratic system may be built from simple linear terms of known dynamics and a multiplier. Computer simulation results on quadratic systems with building elements of various time constants indicate reasonably that the larger time constant term before multiplication dominates in the envelope of the off-diagonal kernel curves as these move perpendicular to and away from the main diagonal. The smaller time constant term before multiplication combines with the effect of the time constant after multiplication to dominate in the kernel curves in the direction of the second-degree impulse response, i.e., parallel to the main diagonal. Such types of insight may be helpful in recognizing essential aspects of (second-degree) kernels; they may be used in simplifying the model structure and, perhaps, add to the physical/physiological understanding of the underlying processes.

To many engineers and scientists, "experts" as well as non-experts in identification, this field presents itself still as "a bag of tricks" due to the great variety of methods advocated by numerous authors. Yet a multitude of methods can be presented as elements in a coherent picture, particularly for model-representations that are linear-in-the-parameters. Such methods can be recognized as belonging to one of three related classes, with specific properties. This leads to increased insight and to new, practical estimation schemes, adaptable for a wide variety of situations.

In electrical and control engineering education the stochastic signal theory is gradually evolving to a middle-undergraduate level. During pre- and early-university training the student has been exposed predominantly to deterministic thinking. This implies that for numerous students the mastering of stochastic concepts is quite a difficult assignment.

In electrical and control engineering education the stochastic signal theory is gradually evolving to a middle-undergraduate level. During pre- and early-university training the student has been exposed predominantly to deterministic thinking. This implies that for numerous students the mastering of stochastic concepts is quite a difficult assignment.

The increasing applicability of system identification, as noticed in purely-engineering problems, can also be recognized in bio-medical tasks. This development of actual applications takes place in spite of the well-known specific limitations and problems related to such bio-systems: -restricted amount of a priori qualitative Knowledge; -very limited (if any) quantitative knowledge; -nonlinearities; -variability in time of the objects; -variability between objects; -poor measurability/observability; -difficult experimental conditions, as well as limitations due to ethical aspects

This paper attempts to summarize some recent essential developments in the field of system identification and parameter estimation. The topics discussed include: the identification protocol, model representations, estimation methods, identification tools, identification applications, expectations on the further developments. It is recognized that identification in an engineering sense has both a ‘science’ and an ‘arts’ aspect