Abstract
Nowadays, artificial intelligence has entered into all spheres of human activity. However, there are some problems in the analysis of objects, for example, there is a priori uncertainty about the state of objects and the analysis takes place in a difficult situation against the background of intentional (natural) interference and uncertainty. The best solution in this situation is to integrate with the data analysis of information systems and artificial neural networks. This paper develops an improved method for finding solutions for neuro-fuzzy expert systems. The proposed method allows increasing the efficiency and reliability of making decisions about the state of the object. Increased efficiency is achieved through the use of evolving neuro-fuzzy artificial neural networks, as well as an improved procedure for their training. Training of evolving neuro-fuzzy artificial neural networks is due to learning their architecture, synaptic weights, type and parameters of the membership function, as well as the application of the procedure of reducing the dimensionality of the feature space. The analysis of objects also takes into account the degree of uncertainty about their condition. In the proposed method, when searching for a solution, the same conditions are calculated once, which speeds up the rule revision cycle and instead of the same conditions of the rules, references to them are used. This reduces the computational complexity of decision-making and does not accumulate errors in the training of artificial neural networks as a result of processing the information coming to the input of artificial neural networks. The use of the proposed method was tested on the example of assessing the state of the radio-electronic environment. This example showed an increase in the efficiency of assessment at the level of 20–25 % by the efficiency of information processing
Highlights
IntroductionEastern-European Journal of Enterprise Technologies ISSN 1729-3774 5/4 ( 107 ) 2020
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].Eastern-European Journal of Enterprise Technologies ISSN 1729-3774 5/4 ( 107 ) 2020This class of intelligent information systems is characte rized by the fact that they are able to model the thinking process of an expert when making a decision and explain why this or that result was obtained
There are some difficulties and problems in objects analyzing: 1) the analysis takes place against the background of intentional and natural disturbances; 2) the interpretation of the obtained results depends on the experience of the decision-maker and the completeness of additional information on a specific task; 3) high dynamics of changes in the state of the object and the environment; 4) a large number of features that characterize the object and the environment; 5) limited time for analysis and decision-making in conditions of uncertainty
Summary
Eastern-European Journal of Enterprise Technologies ISSN 1729-3774 5/4 ( 107 ) 2020 This class of intelligent information systems is characte rized by the fact that they are able to model the thinking process of an expert when 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, about the processes that take place in it, about the laws that govern these processes [3, 5, 7]. There are some difficulties and problems in objects analyzing: 1) the analysis takes place against the background of intentional and natural disturbances; 2) the interpretation of the obtained results depends on the experience of the decision-maker and the completeness of additional information on a specific task (conditions of uncertainty); 3) high dynamics of changes in the state of the object and the environment; 4) a large number of features that characterize the object and the environment; 5) limited time for analysis and decision-making in conditions of uncertainty.
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