1: Introduction --; 1.1 The terminology --; 1.2 The software --; 1.3 The purpose --; 1.4 The experiment facilities --; 1.5 The model structure --; 1.6 The philosophy --; 2: Randomness, probability, and likelihood --; 2.1 Bayes' idea --; 2.2 The information contents of an experiment --; 2.3 Covariation and causality --; 3: The experiment --; 3.1 An introductory example --; 3.2 Requirements for proper experimentation --; 3.4 Dynamic systems --; 3.5 Experiments on dynamic objects --; 4: The identification problem --; 4.1 Validation and falsification --; 4.2 Model structures, data descriptions, and purposive models --; 4.3 Fitting --; 4.4 Basic identification procedures --; 4.5 Conditions for Bayesian validation --; 4.6 The origin of 'pitfalls' --; 5: Modelling --; 5.1 Parametrization --; 5.2 The parameter map --; 5.3 Algorithmic models --; 5.4 The modelling of dynamic systems --; 5.5 Internal and external models --; 5.6 Implicit and explicit models --; 5.7 Finite-memory models --; 5.8 Classification of models by purpose --; 5.9 'Black-box' and 'grey-box' models --; 6: Large-sample theory --; 6.1 Equivalent dynamic models --; 6.2 Consistency --; 6.3 Identifiability --; 6.4 Falsification in the limit --; 6.5 Proper 'black-box' identification --; 6.6 A concluding example --; 7: Validation techniques --; 7.1 Validating parametric models --; 7.2 Large-sample techniques --; 7.3 Two 'pitfalls' --; 8: Falsification techniques --; 8.1 Statistical tests --; 8.2 Unconditional falsification --; 8.3 Conditional falsification of models --; 8.4 Conditional falsification of structures --; 8.5 The Likelihood-Ratio test --; 8.6 Efficiency vs safety --; 9: Structure identification --; 9.1 Using the biassed Likelihood --; 9.2 Sequential falsification --; 9.3 Philosophy revisited: Equivalence vs goodness --; 9.4 Designing the criterion: Description vs purpose --; 9.5 Defining the optimal order: Accuracy vs complexity --; 9.6 Model structure selection --; 9.7 Terminology revisited --; 10: A unified design procedure --; 10.1 Summary of conditions for proper identification --; 10.2 Identification procedures --; 10.3 Procedure for modelling and identification --; References --; Glossary of notations.
SUMMARY OR ABSTRACT
Text of Note
This book aims at giving users of identification software the fundamental insight needed to carry out interactive design of models of physical objects. The book therefore starts with the fundamental conditions for setting up correct identification problems, continues by highlighting the roles of validation and falsification of models, and ends with concrete procedures for interactive design of stochastic dynamic models. The approach is new. A second novelty is that the book does not concentrate on the usual blackbox models. It em- phasizes the purpose of the design and the importance of supplementing experimental data with the partial apriori knowledge that is often available to the designer. The book also emphasizes the prospects and limitations of identification. It clarifies what can and cannot be inferred about the object under various circumstances, and, consequently, what kind of modelling errors the computer can and cannot diagnose. It illuminates the 'pitfalls', i.e. approaches that may appear feasible, but which may easily go wrong.