Abstract
Development of a complex method for finding a solution for neuro-fuzzy expert systems
Highlights
Nowadays, many areas of human activity use artificial intelligence approaches to solve important practical problems.Expert systems have been successfully used in complex technical systems to solve informal or poorly formalized tasks, such as training, diagnostics, forecasting, control and measurement [1,2,3,4,5,6].Mathematics and cybernetics – applied aspectsThis class of intelligent information systems is characterized by the fact that they are able to model the expert’s thinking process in making a decision and explain why this or that result was obtained
This is achieved by implementing the procedure of logical inference on formalized knowledge about the subject area, the processes that take place in it and the laws that govern these processes [2, 7]
The results showed that the proposed system can be used as a powerful diagnostic tool with an accuracy of 93.02 %, specificity of 89.29 %, sensitivity of 95.24 % and accuracy of 92.86 % for the diagnosis of cystic fibrosis
Summary
This class of intelligent information systems is characterized by the fact that they are able to model the expert’s thinking process in making a decision and explain why this or that result was obtained. This is achieved by implementing the procedure of logical inference on formalized knowledge about the subject area, the processes that take place in it and the laws that govern these processes [2, 7]. There are a number of difficulties and problems in analyzing the operational situation: 1. The obtained data do not coincide with the standards due to the influence of different types of interference and incomplete intelligence
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