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Моделирование сложных радиоэлектронных систем на основе интеллектуальных методов анализа данных

Моделирование сложных радиоэлектронных систем на основе интеллектуальных методов анализа данных

Abstract

Рассмотрена возможность применения методов интеллектуального анализа данных для обработки результатов измерений, выполняемых в процессе разработки и конструирования сложных радиоэлектронных систем. В качестве исходных данных использовались результаты измерений, проводимых с целью контроля соответствия параметров системы и отдельных ее компонентов условиям технического задания. На основе измерений сформирована база данных, каждая запись которой представляет собой серию измерений, выполненную для 50 устройств. Собранные данные были проанализированы с помощью интеллектуальных моделей на основе нейронной сети, дерева решений и карт Кохонена с целью обнаружения зависимостей между измеренными параметрами. Знание данных зависимостей позволило выявить мешающие факторы, трудно поддающиеся учету (человеческий фактор, разброс параметров измерительной аппаратуры, особенности условий проведения измерений), уменьшить необходимое число измерений, сократить требуемое количество измерительной аппаратуры. Разработанная аналитическая система реализована на базе платформы Deductor с использованием специализированного хранилища данных. Предложенная конфигурация аналитической системы предусматривает создание отдельных рабочих мест для аналитика и инженерных работников, использующих результаты исследования.

The possibility of using data mining techniques for processing the results of measurements made in the design and construction of complex electronic systems. As initial data we used the results of measurements carried out in order to monitor compliance with the parameters of the system and its individual components terms of technical specifications. On the basis of measurements generated database, each entry of which is a series of measurements performed for 50 devices. The collected data were analyzed using intelligent models based on neural networks, decision trees and Kochonen maps to detect relationships between the measured parameters. Knowledge of these relationships revealed confounding factors that are difficult to account (the human factor, the spread parameters of the measuring equipment, especially measurement conditions), reduce the required number of measurements and the amount of instrumentation. The analytical system is implemented on the platform Deductor using specialized data warehouse. The proposed configuration of the analytical system provides for the creation of jobs for individual analyst and engineering workforce, using the results of the study.

Keywords

РАДИОЭЛЕКТРОННАЯ СИСТЕМА, МОДЕЛИРОВАНИЕ, ЭВРИСТИЧЕСКИЕ МЕТОДЫ, ИНТЕЛЛЕКТУАЛЬНЫЙ АНАЛИЗ ДАННЫХ, МАШИННОЕ ОБУЧЕНИЕ, НЕЙРОННАЯ СЕТЬ, ДЕРЕВО РЕШЕНИЙ, КАРТА КОХОНЕНА, КЛАСТЕРИЗАЦИЯ, MAP KOСHONEN

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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
gold