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Abstract Context. The urgent task of improving the speed of neuro-fuzzy model construction by the precedents has been solved. Objective is a creation of a neuro-fuzzy network synthesis method with high speed of computations and allowing to realize the synthesis of neuro-fuzzy networks in parallel mode. Method. The method of neuro-fuzzy model constructing by precedents, which reduces the dimension of the input data by hashing transformation to the one-dimensional axis saving local cluster topology in a feature space, estimates the significance of the features and instances on the basis of selected clusters, and also forms a partition of the original feature space in an automatic mode, synthesizes structure and adjusts parameters of the neuro-fuzzy model automatically, excluding from the training process of the neuro-fuzzy model the uninformative data, thus simplifying the structure of the obtained model, allows to perform most computationally costly operations in parallel mode, that allows to automate the process of neuro-fuzzy model synthesis by precedents, as well as to increase the speed of neuro-fuzzy model construction both in sequential and in parallel implementation of computations. Results. The software implementing proposed method have been developed and used in computational experiments investigating the properties of the method. The experiments confirmed the efficiency of the proposed method and software. Conclusions. The experiments also allow to recommend them for use in practice to solve the problems of diagnosis and automatic classification by the features. References Субботін С. О. Подання й обробка знань у системах штучного інтелекту та підтримки прийняття рішень : навч. посібник / С. О. Субботін. – Запоріжжя : ЗНТУ, 2008. – 341 с. 2. Круглов В. В. Нейро-нечеткие методы классификации / В. В. Круглов, О. В. Балашов. – М. : Российский университет кооперации, 2009. – 195 с. 3. Computational intelligence: a methodological introduction / [R. Kruse, C. Borgelt, F.Klawonn et. al.]. – London: Springer-Verlag, 2013. – 488 p. DOI: 10.1007/978-1-4471-5013-8_1 4. Интеллектуальные информационные технологии проектирования автоматизированных систем диагностирования и распознавания образов : монография / [С. А. Субботин, Ан. А. Олейник, Е. А. Гофман и др.] ; под ред. С. А. Субботина. – Харьков : Компания СМИТ, 2012. – 318 с. 5. Субботін С. О. Нейронні мережі : навчальний посібник / С. О. Субботін, А. О. Олійник ; під заг. ред. проф. С. О. Субботіна. – Запоріжжя : ЗНТУ, 2014. – 132 с. 6. Гибридные нейро-фаззи модели и мультиагентные технологии в сложных системах: монография / [В. А. Филатов, Е. В. Бодянский, В. Е. Кучеренко и др.] ; под общ. ред. Е. В. Бодянского. – Дніпропетровськ : Системні технології, 2008. – 403 с. 7. Buckleya J. J. Fuzzy neural networks: a survey / J. J. Buckleya, Y. Hayashi // Fuzzy sets and systems. – 1994. – Vol. 66, Issue 1. – P. 1–13. 8. Chai Y. Mamdani Model based Adaptive Neural Fuzzy Inference System and its Application / Y. Chai, L. Jia, Z. Zhang // International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:3, No:3, 2009. – P. 663–670. 9. Mamdani E. H. An experiment in linguistic synthesis with fuzzy logic controller / E. H. Mamdani, S. Assilian // International journal of man-machine studies. – 1975. – Vol. 7, № 1. – P. 1–13. 10. Скобцов Ю. А. Основы эволюционных вычислений / Ю. А. Скобцов. – Донецк: ДонНТУ, 2008. – 330 с.

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