The decision support subsystem in the neural network pattern recognition system, allowing to reduce the subjectivity and increase the quality of expert decisions in the construction of training samples by the statistical data of observations over the state of the management facilities of the production or social environment is developed. A functional structure of the NNPR system of the management facilities of the production or social environment by statistical information is proposed. According to the proposed structure, the problem-oriented NNPR system consists of subsystems of the initial data preparation for the PR and storage of the aggregated data, neural network models, and knowledge about the management facility of the production or social environment, as well as the support of the decision-making about its state. The decision support subsystem based on the proposed method of support of the classification decision-making using the Kohonen self-organizing layer is developed. The developed method of support of the classification decision-making provides a consistent implementation of the stages of self-organizing of neurons of the Kohonen computing layer, the calibration of the elements of the output vector of the training sample and their final labeling. An example of implementation of the proposed subsystem of support of decision-making about the class of the initialized labor protection project based on the expert estimates of the current level of the organization and working conditions at the enterprise is given. Using the developed decision support subsystem in the systems of NNPR by statistical information allows to adjust the decisions of the DM in accordance with the decisions of the neural network output machine, and thereby increase a relative share of correct expert estimates by 20 % on average and reduce false estimates for a number of pattern recognition problems by 50 %. Author Biographies Ольга Сергеевна Маникаева, Odessa National Polytechnic University st. Shevchenko 1, Odessa, Ukraine, 65044 postgraduate Department of Information Systems
Alan : Fen Bilimleri ve Matematik
Dergi Türü : Uluslararası
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