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Применение твинов для описания Ð´Ð°Ð½Ð½Ñ‹Ñ Ð¸ вычислений

выпускная квалификационная работа магистра

Применение твинов для описания Ð´Ð°Ð½Ð½Ñ‹Ñ Ð¸ вычислений

Abstract

Данная работа посвящена разработке и программной реализации математического аппарата для решения задач идентификации масс-спектров важного класса органических веществ — нейромедиаторов с использованием методов интервальной статистики и инструментов интервального анализа. Вычисления проводятся в полной арифметике Каухера. Работа выполнена на языке программирования Python. Ключевыми результатами работы являются представление двух модификаций метода решения переопределённых интервальных систем линейных алгебраических уравнений (ИСЛАУ), а также одновременное получение внутренних и внешних оценок результатов вычислений, результаты представляются в форме составных интервальных объектов — твинов.

This work is devoted to the development and software implementation of a mathematical apparatus for solving problems of identifying mass spectra of an important class of organic substances — neurotransmitters using interval statistics methods and interval analysis tools. Calculations are carried out in Kaucher complete interval arithmetic. The work was performed in the Python programming language. The key results of the work are the presentation of two modifications of the method for solving overdetermined interval systems of linear algebraic equations, as well as simultaneous obtaining internal and external estimates of the calculation results, the results are presented in the form of composite interval objects — twins.

Keywords

метод ÐºÐ²Ð°Ð´Ñ€Ð°Ñ‚Ð½Ñ‹Ñ Ð¼Ð°Ñ‚Ñ€Ð¸Ñ†, нейромедиаторы, Интервальный анализ (мат.), Масс-спектрометрия, twin arithmetic, твинная арифметика, neurotransmitters, square matrices method

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citations
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!
0
Average
Average
Average
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