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Identification of Reality in Bayesian Context
 ComputerIntensive Methods in Control and Signal Processing: Curse of Dimensionality. Birkhauser
, 1997
"... Complexity has many facets as does any general concept. The relationship between "infinitely" complex reality and restricted complexity of the artificial world of models is addressed. Particularly, the paper tries to clarify the meaning of Bayesian identification under mismodelling by answering the ..."
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Cited by 3 (1 self)
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Complexity has many facets as does any general concept. The relationship between "infinitely" complex reality and restricted complexity of the artificial world of models is addressed. Particularly, the paper tries to clarify the meaning of Bayesian identification under mismodelling by answering the question, "What is the outcome of the Bayesian identification without supposing the model set considered contains the "true" system model?" The answer relates known asympotic results to the "natural" finitetime domain of Bayesian paradigm. It serves as an interpretation "smoother" of those Bayesian identification results that quietly ignore the mismodelling present. Keywords: decisionmaking, model selection, Bayesian identification, approximation 1 Introduction System identification can be understood as the set of procedures which model an investigated part of reality (called object, process, plant or system) using data measured on it [8]. Modelling of the reality, often informal, is a n...
Bayes for rolling mills: From parameter estimation to decision support
 In Proceedings of the 16th IFAC World Congress
, 2005
"... Abstract: The paper shares experience gained in application of dynamic Bayesian approach to control problems in the field of metal rolling. The contribution introduces basic notions of theory applied and provides the algorithmic as well as applicationoriented solutions developed. Specifically, the ..."
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Cited by 2 (2 self)
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Abstract: The paper shares experience gained in application of dynamic Bayesian approach to control problems in the field of metal rolling. The contribution introduces basic notions of theory applied and provides the algorithmic as well as applicationoriented solutions developed. Specifically, the consistent use of the approach resulted in an advanced decision support system for operators of a reversing cold rolling mill.
Sequential Sampling Techniques for LogNormal Combination of Probability Densities
"... Ústav teorie informace a automatizace, v.v.i. ..."
Algorithm for Determination of Module Structure of Predicted and/or Controlled Process
 UTIA AV CR, P.O.Box 18, 182 08
, 1995
"... The problem of model structure determination of an examined process is usually necessary prerequsite for successful prediction and/or control of the process. In particular, dynamic, discreteintime, inputoutput, stochastic, parametrized, linearinparameters, with normal white noise, models have b ..."
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The problem of model structure determination of an examined process is usually necessary prerequsite for successful prediction and/or control of the process. In particular, dynamic, discreteintime, inputoutput, stochastic, parametrized, linearinparameters, with normal white noise, models have been used to describe the process. Due to the incompleteness of the available information about it, a certain number of hypotheses about the model structure is formulated and the best (optimal, true) one corresponding to the true model structure (which is supposed to be among them) is searched for. Bayesian statistics proved to be a useful tool for theoretical solution of the presented problem. However, the number of competitive hypotheses appeared to be very large to find between them the best one. Thus, the special algorithm was developed which searches only a "reasonable" subset of all the hypotheses and tries to find the true model structure of the examined process.
MixtureModel Identification in Traffic Control Problems
"... This paper represents only the first step in the indicated direction. It deals only with identification of a single traffic flow (it detects only the traffic state in a specific position of a single road). The further development will aim to the identification of some more complex situation (crossro ..."
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This paper represents only the first step in the indicated direction. It deals only with identification of a single traffic flow (it detects only the traffic state in a specific position of a single road). The further development will aim to the identification of some more complex situation (crossroads and area of crossroads) and finally to the control of traffic areas. 19 8 Appendix I. Selection of initial conditions
Smoothing Preserving Discontinuity Based on Alternative Models of Parameter Development
"... This paper presents experiments with tracking timevarying parameters of a dynamic system applied to parameter estimation and smoothing of piecewise continuous function. The smoothing algorithm is based on bayesian computation of the probability distribution over the set of alternative hypotheses d ..."
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This paper presents experiments with tracking timevarying parameters of a dynamic system applied to parameter estimation and smoothing of piecewise continuous function. The smoothing algorithm is based on bayesian computation of the probability distribution over the set of alternative hypotheses describing a set of alternative models of expected (possible) parameter development. The sequence of optimal hypotheses can be interpreted as a solution of the continuity fault detection problem. The properties of the algorithm are illustrated on the simulated and real range data. 1 Introduction Extended Kalman filter is generally used for simultaneous parameter tracking and state estimation in timevarying systems. I our case the augmented state of the system consists of parameter state vector only. To achieve the parameter tracking ability in the case of time varying parameters, some form of obsolete information forgetting must be incorporated into the identification algorithm. The forg...
LogNormal Merging for Distributed System Identification
"... Abstract Growing interest in applications of distributed systems, such as multiagent systems, increases demands on identification of distributed systems from partial information sources collected by local agents. We are concerned with fully distributed scenario where system is identified by multipl ..."
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Abstract Growing interest in applications of distributed systems, such as multiagent systems, increases demands on identification of distributed systems from partial information sources collected by local agents. We are concerned with fully distributed scenario where system is identified by multiple agents, which do not estimate state of the whole system but only its local ‘state’. The resulting estimate is obtained by merging of marginal and conditional posterior probability density functions (pdf) on such local states. We investigate the use of recently proposed nonparametric lognormal merging of such ‘fragmental ’ pdfs for this task. We derive a projection of the optimal merger to the class of weighted empirical pdfs and mixtures of Gaussian pdfs. We illustrate the use of this technique on distributed identification of a controlled autoregressive model.
Distributed Bayesian DecisionMaking: Further Experiments
, 2007
"... Decentralized adaptive control is based on the use of many local controllers in parallel, each of them estimating its own local model and pursuing local aims. When each controller designs its strategy using only its model, the resulting control will be suboptimal since its local model is not predict ..."
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Decentralized adaptive control is based on the use of many local controllers in parallel, each of them estimating its own local model and pursuing local aims. When each controller designs its strategy using only its model, the resulting control will be suboptimal since its local model is not predicting consequences of actions of the neighbors. We propose to improve this by exchange multistep predictors on common data between the neighboring controllers and their subsequent use in the design of control strategy. Care is taken to assure that the resulting design procedure is of the same complexity as the one without the exchange. Performance of the approach is illustrated on a simple example. 1
On Estimation of Unknown Disturbances of NonLinear StateSpace Model Using Marginalized Particle Filter
"... Ústav teorie informace a automatizace, v.v.i. ..."