The article describes a technique for constructing a non-fuzzy model for selecting contour points on an image. The technique includes the following steps: the formation of linguistic variables “pixel brightness difference” and “a sign that a pixel belongs to a contour”, the formation of a knowledge base of a neuro-fuzzy model using a binary image, the formation of a training set using both grayscale and contour images, training a neuro-fuzzy model using genetic algorithm. A feature of the presented genetic algorithm is - checking the conditions for the correctness of the values of the parameters of the membership functions obtained during the generation of chromosomes. Described the structure of a neuro-fuzzy model for making a decision about whether a pixel belongs to a contour. Presented the result of applying a neuro-fuzzy model for constructing image contours.

Keywords: neuro-fuzzy model, contour image, contour extraction, contour pixel, linguistic variable, fuzzy set, membership function, genetic algorithm, Tsukamoto inference, neuro-fuzzy model learning

The main maintenance of a diversification of production as activity of subjects of managing is considered. being shown in purchase of the operating enterprises, the organizations of the new enterprises, redistribution of investments in interests of the organization and development of new production on available floor spaces. The most important organizational economic targets of a diversification of management are presented by innovative activity of the industrial enterprise.

Keywords: diversification of management, production diversification, financial and economic purposes of a diversification, technological purposes of ensuring flexibility of production