Complex classifiers of formal classification theory of technical systems: hierarcho-matrix, zone, cyclic

The article highlights the different aspects of complex technical systems that can be ordered and classified in accordance with the totality, structure and values of the attributes characterizing these systems by a unified approach to various types of classifications. The most complex classifiers studied in this work are hierarchical-matrix, cyclic and zonal classifiers. Zones are the areas identified in the space of classification attributes that characterized by a similar value of an additional target classification attribute. The dimension of the zonal classification is equal to the number of descriptive classification attributes, the zonal dimension is equal to the number of zones. Adding the zones is carried out according to the target classification criterion, multiplying the zones consists in introducing the new descriptive classification attributes. Cyclicity is repetition of the similar elements that occurs in the space of physical quantities or other parameters. The concept of cycle stages is defined for all cycles, which is a specific (target) classification attribute. The internal dimension of the cycle is equal to the number of stages, the external dimension is equal to the number of acts of the cycle, the descriptive dimension is equal to the number of descriptive classification attributes. Addition of cycles can be carried out both by stages and by descriptive features and consists in increasing the number of values of classification attribute. Multiplication of cycles consists in the introduction of new descriptive classification attributes. Zonal and cyclic classifiers are widely used in the practice of describing and planning technical energy systems. A wide range of classifiers ordered from the standpoint of a unified formal theory of classification will take into account the features of specific technical systems, the conditions for the objects functioning, the context of the interpretation area. As a result, the degree of adequacy of classifiers to the diversity of the interpretation area objects and the representativeness of models based on classifiers will increase.