Open AccessFuzzy model for assessing the organizational effect of an intelligent process control system
Author(s) -
В. А. Смирнов,
В. М. Милова,
М. С. Смирнова,
I. V. Matelenok,
N. A. Zhilnikova
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1047/1/012142
Subject(s) - computer science , fuzzy logic , process (computing) , expert system , complex system , range (aeronautics) , inference , decision support system , management science , artificial intelligence , data mining , machine learning , knowledge management , engineering , aerospace engineering , operating system
The article considers the problem of assessing the organizational effect of the functioning of an intelligent decision support system for controlling complex technical systems at the level of a technological control system. The level of organization of activity is used as an indicator of the organizational effect of an intellectual system. To obtain the numerical values of individual indicators of organizational effects, a system of mathematical models is presented that takes into account the specifics of the subject area. Aggregation of heterogeneous indicators to obtain generalized and complex indicators, which are measured on different scales and have a different range of values, is based on a fuzzy classification of parameter values and a fuzzy inference model using the Takagi-Sugeno algorithm. The obtained quantitative assessment of the organizational effect is complemented by a qualitative assessment containing a linguistic description of the level of organization of activities and the degree of expert’s confidence in the result. The considered system of mathematical models for calculating the indicators of the organizational effect can be supplemented depending on the goals, the degree of detail and the depth of analysis and used in the analysis of the effectiveness of existing and future decision support systems and automated information systems at all stages of the life cycle.