UDC 004.89
Authors
Bulygina Olga V.
The branch of National Research University "MPEI" in Smolensk, Department of Information
Technologies in Economics and Management, Smolensk, Russia
Ivanova Olesya A.
Pushchino State Institute of Natural Sciences, Pushchino, Russia
Abstract
Today, the management of complex innovative projects to create high-tech products is carried out under conditions of information uncertainty. This is due to the fact that the information used to support decision-making often does not have such properties as completeness, consistency, accuracy, reliability. As a result, various risk situations arising during the implementation of subprojects and their stages may not be taken into account in project planning. So, the simultaneous occurrence of a certain set of risks can lead to the project failure. To solve this problem, it is advisable to identify and evaluate NON-Factors that are sources of project risks and do not have one of the properties of classical knowledge models. To identify NON-Factors, it is proposed to use a neural fuzzy classifier that will categorize them based on the analysis of available information and expert opinions. When assessing the risks of the project internal and external environment, it is necessary to take into account the possibility of a systemic effect from the triggering of a certain set of NON-Factors. The analysis of systemic effects will be carried out using the emergence coefficient, the values of which will determine the choice of tools for assessing NON-Factors: in the case of a low value, it is proposed to use fuzzy inference according to Mamdani algorithm, otherwise – fuzzy pyramidal networks. To combine the results of the assessment of NON-Factors identified in the internal and external project environment, fuzzy-logical inference according to Larsen algorithm will be used
Keywords
Project management, Risk, NON-factor, Neural fuzzy classifier, Fuzzy pyramidal network, Emergence index
