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Рассматривается многомерный паттерн поведения как основа логико-лингвистической модели, позволяющей описывать взаимовлияния контролируемых параметров при мониторинге. Приведены архитектура системы мониторинга и алгоритм оценки адекватности модели.Ключевые слова: логико-лингвистическая модель, паттерн поведения, мониторинг, адекватность, прогнозирование состояний.

The article proposes a technique for estimating the maximum possible errors in measuring the parameters of the signals observed against a background of interference that do not exhibit statistical properties on the measurement interval and of which only the region of their possible change is known. This region is a layer within which the interference-free signal can vary due to the variation in the values of the parameters of the model function describing it. The technique is also proposed that makes it possible to estimate the measurement error of the monitored parameter for a specific implementation of the signal described by the proposed model with a confidence probability close to unity. An example of the use of the offered techniques for estimating the coordinate of a light stain created by a point source is shown. The offered approach is actual, when each measurement represents special worth and there is no possibility to improve the measurement result by averaging over the set of signal realizations.

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ObjectivesThe aim of the research is to develop the principle of storing data templates to take their temporal natureinto account, making it possible to reduce decision-making times.In order to describe and identify temporal patterns in fuzzy time series behaviour in real time, the task was set to develop a hybrid data structure that allows for a consideration of sequences of fuzzy values formed from clear observable data as well as a determination of the length of these sequences and possible uneven time intervals between the observations.MethodsThe article discussesan approach to formalising the description of temporal cause-effect relationships between events occurring at the object location as well as that of its environment, based on a set of singly-connected lists of triplets. Each triplet contains a fuzzy linguistic variable, the duration of its observation and the permitted interval of observation of insignificant data.ResultsAn algorithm for detecting knowledge base patterns in real time was developed, taking into account the possibility of a time shift in observing long sequences of identical values of the observed value. The possibility of partial data overlapping corresponding to triplets of different patterns is taken into account. The proposed hybrid pattern makes it possible to accelerate the detection of temporal regularities in the data.ConclusionScientific results are presented by the developed structure for storing information on temporal regularities in data, based on a singly linked linear list, as well as an algorithm for finding regularities in observational data using a set of OLS-patterns. The advantage of this structure and algorithm in comparison with the known ways of storing and analysing temporal data is a reduction in the amount of memory necessary for storing templates in the knowledge base, as well as the possibility of applying OLS patterns for decisionmaking purposes.