Özet:

Abstract The problem of mathematical support development is solved to automate the extraction knowledge as production rules from the training data samples. The object of study is the process of constructing models of non-destructive quality control. The subject of study are methods of production rules extraction based on negative selection for synthesis of quality control models. The purpose of the work is to develop a method of production rules synthesis on the basis of a set of detectors is in the handling of data of training sample, characterized by a substantial number of instances of distinction belonging to different classes. A method for the synthesis of production rules on the basis of negative selection in the case of uneven distribution of instances of the sample classes is proposed. The developed method allows to exclude irrelevant and redundant features from the sample, thereby reducing the search space and time of execution of the method, as well as generate a set of detectors with high approximation and generalization capability. The proposed method improves the generalizing properties of synthesized model and its interpretability. The software implementing proposed method is developed. The experiments to study the properties of the proposed method are conducted. The experimental results allow to recommend the proposed method for use in practice. References Ding S. X. Model-based fault diagnosis techniques: design schemes, algorithms, and tools / S. X. Ding. – Berlin : Springer, 2008. – 473 p. DOI: 10.1007/978-3-540-76304-8. 2. ASM handbook. – Vol. 17: Nondestructive evaluation and quality control. – Cleveland : ASM International, 1997. – 1607 p. 3. Diagnosis and fault-tolerant control / [M. Blanke, M. Kinnaert, J. Lunze, M. Staroswiecki]. – Berlin : Springer, 2006. – 672 p. DOI: 10.1007/978-3-662-05344-7. 4. Price C. Computer based diagnostic systems / C. Price. – London : Springer, 1999. – 136 p. DOI: 10.1007/978-1-4471-0535-0. 5. Denton T. Advanced automotive fault diagnosis / T. Denton. – London : Elsevier, 2006. – 271 p. DOI: 10.4324/9780080462585. 6. Ukil A. Intelligent Systems and Signal Processing in Power Engineering / A. Ukil. – Berlin : Springer, 2007. – 372 p. DOI:10.1007/978-3-540-73170-2. 7. Ishida Y. Immunity-based systems: a design perspective / Y. Ishida. – Berlin : Springer, 2004. – 177 p. DOI: 10.1007/978-3-662-07863-1. 8. Segel L. A. Design principles for immune system and other distributed autonomous systems / L. A. Segel, I. R. Cohen. – New York : Oxford University Press, 2001. – 428 p. 9. Flower D. In silico immunology / D. Flower, J Timmis. – New York : Springer, 2007. – 451 p. DOI: 10.1007/978-0-387-39241-7. 10. A new cluster based real negative selection algorithm / W. Chen, T. Li, J. Qin [et al.] // Information and automation. – 2011. – Vol. 86. – P. 125–131. DOI: 10.1007/978-3-642-19853-3_18.

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