Two mathematical models have been built to describe the behavior of an information system with a client-server architecture without and taking into account the ultimate reliability of the monitoring system and the restoration of the failed component of the system under consideration. The behavior of the information system in the presence of failures and the restoration of the performance of components is approximated by a Markov process and is described by a system of differential equations with variable coefficients. The solution of the obtained differential equations by numerical methods on computers allows the research of various characteristics of the reliability of information systems such as “client-server” in a wide range of changes in failure rates and restoration of system components.
The article provides a classification of existing forecast models the generation of electricity by solar power plants and discusses various options for forecast methods for each of the selected models. As a result of the study, it was concluded that the most promising forecast methods are hybrid statistical-adaptive methods.
A concept of the live unit as an automatic regulation system with a few admissible states areas in the space of states is considered. Energetic profit of oscillatory behavior consisting in the consecutive transitions of system from one admissible states area to another is shown. It is stated, that external disturbances cause the energy consumption of oscillatory system to decrease. On the basis of this concept and some neurophysiological data, the "live" energy-consuming nonlinear three-state neuron model is proposed and the existence of energy optimal generation frequency v(opt) is proved. For the realization of tendency to v(opt) the optimal learning rule is proposed, which provides unsupervised learning and interlinked short-term and long-term memories with forgetting. The model proposed explains the genesis of neural network, is promising in the sense of network self-organization and allows to solve the problem of internal activity in the researches on artificial intelligence.
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