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Нейросетевой метод оптимизации инновационных технологий

Нейросетевой метод оптимизации инновационных технологий

Abstract

Использование методов искусственного интеллекта в технологических задачах позволяет, осуществлять многокритериальную оптимизацию перспективных и проектных технологических процессов, а также дает возможность выбора и трансферта наилучших инновационных, высоких и критических технологий. Использование названных методов не только сокращает трудоемкость работ в рамках проблемно-ориентированной АСТПП по технологической подготовке производства новой конкурентоспособной продукции, но также позволяет находить оптимальные решения при разработке комплектов проектной технологической документации, необходимой для реконструкции и технического перевооружения действующего производства.

The use of artificial intelligence methods in technological tasks allows multiobjective optimization perspective and design processes. These methods make it possible to select and transfer the best of innovative, high and critical technologies. Using these methods, not only reduces the complexity of work within the problem-oriented automated system for pre-production, but also allows us to find optimal solutions for the time-processing complete sets of technical documentation, which is necessary for the reconstruction and technical re-equipment of production.

Keywords

НЕЙРОСЕТЕВЫЕ МЕТОДЫ, ВЕРОЯТНОСТНО-РЕКУРРЕНТНЫЙ МЕТОД, МНОГОКРИТЕРИАЛЬНАЯ ОПТИМИЗАЦИЯ, ИНФОРМАЦИОННАЯ МОДЕЛЬ, ВЕРОЯТНОСТНЫЕ РАСПРЕДЕЛЕНИЯ, ПЕРСПЕКТИВНЫЕ ТЕХНОЛОГИЧЕСКИЕ ПРОЦЕССЫ, РИСКИ.

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Powered by OpenAIRE graph
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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