Informatica

Robust stability results for nominally linear hybrid systems are obtained from total stability theorems for purely continuous-time and discrete-time systems. The class of hybrid systems dealt with consists of, in general, coupled continuous-time and digital systems subject to state perturbations whose nominal (i.e., unperturbed) parts are linear and time-varying, in general. The obtained sufficient conditions on robust stability are dependent on the values of the parameters defining the over-bounding functions of the uncertainties and the weakness of the coupling between the analog and digital sub-states provided that the corresponding uncoupled nominal subsystems are both exponentially stable.

In the practice of metal treatment by cutting it is frequently necessary to deal with self-excited oscillations of the cutting tool, treated detail and units of the machine tool. In this paper are presented differential equations with the delay of self-excited oscillations. The linear analysis is performed by the method of D-expansion. There is chosen an area of asymptotically stability and area D2. It is prove that, in the area D2 the stable periodical solution appears. The non-linear analysis is performed by the theory of bifurcation. The computational experiment of metal cutting process and results of these experiments are presented.

This paper considers some aspects of using a cascade-correlation network in the investment task in which it is required to determine the most suitable project to invest money. This task is one of the most often met economical tasks. In various bibliographical sources on economics there are described different methods of choosing investment projects. However, they all use either one or a few criteria, i.e., out of the set of criteria there are chosen most valuable ones. With this, a lot of information contained in other choice criteria is omitted. A neural network enables one to avoid information losses. It accumulates information and helps to gain better results when choosing an investment project in comparison with classical methods. The cascade-correlation network architecture that is used in this paper has been developed by Scott E. Fahlman and Cristian Lebiere at Carnegie Mellon University.

New formulae providing exact explicit solutions for a class of scalar delayed systems are given.

The principles of a neural network environmental model are proposed. The principles are universal and can use different neural network architectures. Such a model is self-organizing, it can operate in both regimes with and without a teacher. It codes information about objects, their features, the actions operating in an environment, analyzes concrete situations. There are functions for making an action plan, for action control. The goal of the model is given from an external site. The model has more than sixteen active regimes. The neural network environmental model is fulfilled in software and hardware tools.