FUZZY KNOWLEDGE BASE SYNTHESIS OF THE EXPERIENCE LEVEL CLASSIFICATION OF AVIATION SECURITY SCREENERS USING SUB-TRACTIVE CLUSTERING AND ANFISTRAINING

This paper describes a new approach to the estimate reliability improvement of the competence related to the visual searching for prohibited items by aviation security screeners consisting in the application of fuzzy knowledge bases and equipmentspecific diagnostic techniques of the psychophysiological state. As equipment-specific methods it is suggested using the eye-tracking technology giving an opportunity to take into consideration visual searching strategies and the variational cardiointervalometry method giving an opportunity to estimate the operator's psychophysiological strain. This paper considers the theoretical basis of the automatic synthesis of both Sugeno and Mamdani fuzzy knowledge bases. It is demonstrated that the identification of fuzzy knowledge bases using clustering algorithms consists of cluster forming in the dataspace and cluster transforming into fuzzy rules describing the certain part of the investigated system behavior. Experiments were carried out to test the suggested approach. The quality of the synthetized Sugeno fuzzy knowledge base was compared with the Mamdani knowledge base and with the linear regression model. Results showed that this model based on the subtractive clustering and the ANFIS-training with respect to the root-mean-square error does better apprise the investigated dependence than other models. The training sample accuracy was 0.0049. The test sample accuracy was 0.0275. The application of fuzzy knowledge bases will give an opportunity to create decision-making support systems to estimate the competence level of aviation security screeners in intellectual simulator complexes