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Журнал «Агропанорама»
Article . 2023 . Peer-reviewed
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Стохастическое моделирование технологических процессов растениеводства с использованием ортогональных многочленов

Стохастическое моделирование технологических процессов растениеводства с использованием ортогональных многочленов

Abstract

Разработана методология стохастического моделирования технологических процессов, описываемых однофакторными уравнениями в ортогональных многочленах, являющаяся эффективным инструментом изучения сложных технических систем методом системного анализа. Уравнения такого типа позволяют корректно определить минимально достаточный порядок адекватного алгебраического полинома, независимо оценить вклад каждого фактора, а также рассчитать абсолютную ошибку параметра оптимизации. Разработанная методология проиллюстрирована при решении типовой задачи растиниеводства - построение зависимости недобора урожая зерновых при отклонения срока посева от оптимального значения. The method of stochastic modeling of technological processes described by single factor equations in orthogonal polynomials, which is an effective tool for studying complex technical systems by the method of system analysis, has been developed. The type of equations allows us to determine corretly the minimum sufficient order of an adequate algebraic polynomial, to evaluate independetly the contribution of each factor, as well as to calculate the absolute error of the optimization parameter. The method is illustrated when solving the typical problem of crop production - establishing a relationship between insufficient crop harvest and optimal value deviation.

Keywords

технологические процессы растениеводства, моделирование технологических процессов, недобор урожая, урожайность зерновых культур, orthogonal polynomial, simulating function, 630, simulated function, 510, absolute error of simulating function, сроки посева, insufficient harvest, системный анализ, моделируемые функции, ортогональные многочлены, моделирующие функции, производство зерна, operation time

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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!
0
Average
Average
Average
Green