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Procedure for Constructing Soft Models of Complex Systems by Time Series

Authors: Suyatinov, S.I.;

Procedure for Constructing Soft Models of Complex Systems by Time Series

Abstract

S.I. Suyatinov,Bauman Moscow State Technical University, Moscow,Russian Federation, ssi@bmstu.ru Сергей Игоревич Суятинов, кандидат технических наук, доцент, кафедра≪Системы автоматического управления≫, Московский государственный технический университет им. Н.Э. Баумана (г. Москва, Российская Федерация), ssi@bmstu.ru. The problem of creating models of complex systems for assessing their state isconsidered. The analysis of approaches to construction of diagnostic models is given andtheir features are marked. For a complex system with a hierarchical structure, a procedurefor constructing the models to assess its state using a scalar time series is proposed. Inthis case, each hierarchical level is described by a lumped-parameter differential equation.The procedure is based on the concept of soft modelling. The efficiency of the proposedprocedure is demonstrated by the example of constructing a model for assessing the stateof a complex heart rhythm regulation system. Рассмотрена проблема создания моделей сложных систем для оценки их состояния. Приведен анализ подходов к построению диагностических моделей и отмечены их особенности. Для сложной системы, имеющей иерархическую структуру, предложена процедура конструирования по скалярному временному ряду моделей для оценки ее состояния. При этом каждый иерархический уровень описывается дифференциальным уравнением с сосредоточенными параметрами. В основу процедуры положена концепция мягкого моделирования. Работоспособность предложенной процедуры продемонстрирована на примере конструирования модели для оценки состояния сложной системы регуляции сердечного ритма.

Keywords

сложная система, basic models, cardiovascular system, мягкое моделирование, базовые модели, сердечно-сосудистая система, complex system, УДК 530.18, soft modelling

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    popularity
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    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
Green
gold