Dynamical processes and correlations at midlatitudes in the lower and middle atmosphere

The wave structure of the zonal circulation has been investigated within the height intervals 1,5 - 12 km, 1,5 - 22,5 km and 80 - 110 km for the spectral region corresponding to the time scales characteristic for the planetary waves (2-30 days). The coherent wave structures in the lower and middle atmosphere have been found to be seasonally and interannually dependent and also show variations with height. © 2001 COSPAR. Published by Elsevier Science Ltd. All rights reserved

Dynamical processes and correlations at midlatitudes in the lower and middle atmosphere

The wave structure of the zonal circulation has been investigated within the height intervals 1,5 - 12 km, 1,5 - 22,5 km and 80 - 110 km for the spectral region corresponding to the time scales characteristic for the planetary waves (2-30 days). The coherent wave structures in the lower and middle atmosphere have been found to be seasonally and interannually dependent and also show variations with height. © 2001 COSPAR. Published by Elsevier Science Ltd. All rights reserved

Dynamical processes and correlations at midlatitudes in the lower and middle atmosphere

The wave structure of the zonal circulation has been investigated within the height intervals 1,5 - 12 km, 1,5 - 22,5 km and 80 - 110 km for the spectral region corresponding to the time scales characteristic for the planetary waves (2-30 days). The coherent wave structures in the lower and middle atmosphere have been found to be seasonally and interannually dependent and also show variations with height. © 2001 COSPAR. Published by Elsevier Science Ltd. All rights reserved

Dynamical processes and correlations at midlatitudes in the lower and middle atmosphere

The wave structure of the zonal circulation has been investigated within the height intervals 1,5 - 12 km, 1,5 - 22,5 km and 80 - 110 km for the spectral region corresponding to the time scales characteristic for the planetary waves (2-30 days). The coherent wave structures in the lower and middle atmosphere have been found to be seasonally and interannually dependent and also show variations with height. © 2001 COSPAR. Published by Elsevier Science Ltd. All rights reserved

Method of knowledge base training of intellectual real - time system based on the algorithm of decision tree

Работа посвящена разработке математического алгоритма самообучения интеллектуальной системы (ИС) управления наукоемкими проектами создания сложных технических объектов. Обучение ИС происходит путем оптимизации правил базы знаний, используя методы машинного обучения. Это позволит повысить эффективность поддержки принятия решений при реализации наукоемких проектов и адаптации рекомендаций ИС под запрос лица принимающего решение. На основе анализа и разметки имеющихся данных, а также сформулированных требований к алгоритму, выбран алгоритм деревья регрессии и классификации (CART) в силу высокой скорости его работы и возможностей формализации полученных зависимостей в формате продукционных правил. В соответствии с выставленными требованиями определены параметры обучения и проведена проверка эффективности работы алгоритма классификации научных проектов на заданное число классов и формулирования новых зависимостей базы знаний. Представлена общая схема обработки запроса пользователя описанным функционалом интеллектуального модуля. The development of a mathematical algorithm for the self-learning of the intellectual system (IS) for the management of science-intensive projects for the creation of complex technical objects is considered. IS training occurs by optimizing the knowledge base rules using machine learning methods. This will increase the effectiveness of decision support in the implementation of science-intensive projects and the adaptation of the recommendations of the IS on the request of the decision-maker. Based on the analysis and markup of the available data, as well as the formulated requirements for the algorithm, the algorithm of regression and classification (CART) trees is chosen because of its high speed and the possibilities of formalizing the obtained dependencies in the format of production rules. In accordance with the requirements set, the training parameters were determined and the efficiency of the algorithm for classifying scientific projects for a given number of classes and for formulating new dependencies of the knowledge base was checked. A general scheme for processing the user's request with the described functionality of the intelligent module is presented.Работа выполнена в рамках гранта ФГБОУ ВО «ИжГТУ имени М.Т. Калашникова» No 09.04.02/18ГММ