ANALYSIS OF THE EFFECTIVENESS OF TECHNOLOGICAL INNOVATIONS BY FUZZY MODELING METHODS: THE CONTENT OF THE MODEL AND THE CONDITIONS FOR SOFTWARE IMPLEMENTATION

Abstract

The article is devoted to the development of methods and instruments for solving the problem of non-stochastic uncertainty in the management of technological innovations. In this regard, the methodology of fuzzy-multiple modeling of systems is considered. It allows you to take into account both deterministic and stochastic data, as well as mental knowledge of the system on the part of decision makers, presented in the lexical description and based on fuzzy evaluation mechanism. The construction of fuzzy-multiple models is aimed at reproducing the logic of decision making and is based on the use of intelligent methods of information processing, including those presented in fuzzy and verbal characteristics, by mathematical language means, which can be transferred to machine processing. The basis of the study is the provision on the vector form of the efficiency indicator and the implementation of the correspondence function between the basic parameters of efficiency through fuzzy inference. The article describes the developed basic conditions for simulation of fuzzy-multiple modeling in assessing the effectiveness of technological innovation management systems - the structure of the model and methods for its construction; presents the means of software implementation of a simulation fuzzy-plural model developed in accordance with these conditions and the results of its practical testing. The developed conditions of fuzzy-multiple modeling in assessing the effectiveness of technological innovations form the basis of a comprehensive analysis of the conditions of technological development of the enterprise, allow to identify significant management factors and form the content of an effective innovative strategy for the technological development of the enterprise; the fuzzy model itself can be considered as a platform for the integration of deterministic, stochastic, expert knowledge of the system.