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Hintergrund: Die Zunahme des medizinischen Wissens, quantifiziert durch die Anzahl wissenschaftlicher Publikationen, nimmt rasant zu. Pro Monat erscheinen etwa 67.000 neue Artikel, über 3300 davon sind klinische Studien. Die Zunahme erfolgt exponentiell. Die relevante wissenschaftliche Literatur[for full text, please go to the a.m. URL], EbM – ein Gewinn für die Arzt-Patient-Beziehung?; Forum Medizin 21 der Paracelsus Medizinischen Privatuniversität & 11. EbM-Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin

An essential limitation in using the classical optimal control has been its limited robustness to modeling inadequacies and perturbations. This paper presents conceptions of two practical control structures based on the time-optimal approach: hard and soft ones. The hard structure is defined by parameters selected in accordance with the rules of the statistical decision theory; however, the soft structure allows additionally to eliminate rapid changes in control values. The object is a basic mechanical system, with uncertain (also non-stationary) mass treated as a stochastic process. The methodology proposed here is of a universal nature and may easily be applied with respect to other elements of uncertainty of time-optimal controlled mechanical systems.

If a mechanical system experiences symmetry, the Lagrangian becomes invariant under a certain group action. This property leads to substantial simplification of the description of movement. The standpoint in this article is a mechanical system afected by an external force of a control action. Assuming that the system possesses symmetry and the configuration manifold corresponds to a Lie group, the Euler-Poincare reduction breaks up the motion into separate equations of dynamics and kinematics. This becomes of particular interest for modeling, estimation and control of mechanical systems. A control system generates an external force, which may break the symmetry in the dynamics. This paper shows how to model and to control a mechanical sys- tem on the reduced phase space, such that complete state space asymptotic stabilization can be achieved. The paper comprises a specialization of the well-known Euler-Poincare reduction to a rigid body motion with forcing.

If a mechanical system experiences symmetry, the Lagrangian becomes invariant under a certain group action. This property leads to substantial simplification of the description of movement. The standpoint in this article is a mechanical system affected by an external force of a control action. Assuming that the system possesses symmetry and the configuration manifold corresponds to a Lie group, the Euler-Poincaré reduction breaks up the motion into separate equations of dynamics and kinematics. This becomes of particular interest for modelling, estimation and control of mechanical systems. A control system generates an external force, which may break the symmetry in the dynamics. This paper shows how to model and to control a mechanical system on the reduced phase space, such that complete state space asymptotic stabilization can be achieved. The paper comprises a specialization of the well-known Euler-Poincaré reduction to a rigid body motion with forcing.

If a mechanical system experiences symmetry, the Lagrangian becomes invariant under a certain group action. This property leads to substantial simplification of the description of movement. The standpoint in this article is a mechanical system affected by an external force of a control action. Assuming that the system possesses symmetry and the configuration manifold corresponds to a Lie group, the Euler-Poincaré reduction breaks up the motion into separate equations of dynamics and kinematics. This becomes of particular interest for modelling, estimation and control of mechanical systems. A control system generates an external force, which may break the symmetry in the dynamics. This paper shows how to model and to control a mechanical system on the reduced phase space, such that complete state space asymptotic stabilization can be achieved. The paper comprises a specialization of the well-known Euler-Poincaré reduction to a rigid body motion with forcing.

The paper adopts the energy shaping method to control of rotational motion. A global representation of the rigid body motion is given in the canonical form by a quaternion and its conjugate momenta. A general method for motion control on a cotangent bundle to the 3-sphere is suggested. The design algorithm is validated for three-axis spacecraft attitude control. Udgivelsesdato: APR

The present paper deals with the time-optimal control for mechanical systems with uncertain load. A fuzzy approach is used in the design of suboptimal feedback controllers, robust with respect to the load. Statistical kernel estimators are used for the specification of crucial parameters. The methodology proposed in this work may be easily adopted to other modeling uncertainties of mechanical systems, e.g. motion resistance.

The paper adopts the energy shaping method to control of rotational motion. A global representation of the rigid body motion is given in the canonical form by a quaternion and its conjugate momenta. A general method for motion control on a cotangent bundle to the 3-sphere is suggested. The design algorithm is validated for three-axis spacecraft attitude control

The problem of estimating conditional quantiles using neural networks is investigated here. A basic structure is developed using the methodology of kernel estimation, and a theory guaranteeing con-sistency on a mild set of assumptions is provided. The constructed structure constitutes a basis for the design of a variety of different neural networks, some of which are considered in detail. The task of estimating conditional quantiles is related to Bayes point estimation whereby a broad range of applications within engineering, economics and management can be suggested. Numerical results illustrating the capabilities of the elaborated neural network are also given.

Neural networks for estimating conditional distributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency is proved from a mild set of assumptions. A number of applications within statistcs, decision theory and signal processing are suggested, and a numerical example illustrating the capabilities of the elaborated network is given

The problem of parameter identification by Bayes point estimation using neural networks is investigated....

An essential limitation in using the classical optimal control has been its limited robustness to modeling inadequacies and perturbations. This paper presents conceptions of two practical control structures based on the time-optimal approach: hard and soft ones. The hard structure is defined by parameters selected in accordance with the rules of the statistical decision theory; however, the soft structure allows additionally to eliminate rapid changes in control values. The object is a basic mechanical system, with uncertain (also non-stationary) mass treated as a stochastic process. The methodology proposed here is of a universal nature and may easily be applied with respect to other elements of uncertainty of time-optimal controlled mechanical systems.