Improving Fuzzy Network Models For the Analysis of Dynamic Interacting Processes in the State Space

Abstract

Most promising approaches that deal with dynamics of interacting processes do not take into consideration the fuzzy properties of all components and the state space of the model, the set of parameters and features of the subject domain. This leads us to create integrated systematic approach to solve these problems as a sole task for developing new effective mathematical fuzzy models that take into account the advantages of existing models, formal criteria and approaches, modern technologies, methods and tools for solving theoretical and practical tasks. The extended fuzzy Petri Networks and their further development are proposed with new capabilities to solve the analysis and adaptation problems depending on the peculiarities of the processes of the subjected area. The proposed approach which is mainly based on some general provisions for the development of color Petri nets and fuzzy colored Petri dishes, tends to create a new class of fuzzy Petri nets to effectively solve complex tasks while reducing the dimension of the model and complexity. The implementation of the model is focused on the use of modern information technologies. Colored Petri nets can be considered an effective tool for determining the size of network models. Limitations on the homogeneity of logical operations are presented taking into account fragments of fuzzy network models. This can be done by practical use of the presented models..

Information Technologies of the Processing of the Spaces of the States of a Complex Biophysical Object in the Intellectual Medical System HEALTH

“…Determination of the coefficient of confidence at some interval ) (k KU j  given (9), it has the form:…”

“…Since the choice of decision in medicine is made in the context of uncertainty about the symptomatology, which in fact implies consideration of a broader list of potential solutions, it is advisable to limit the initial set of potential decisions based on fuzzy preference. Thus, the realization of this task is to determine some subset of decisions on the basis of formal evaluation and formation of each decision using the provisions and ideas of computational intelligence [9]- [11].…”

“…Today, the use of intelligent decision-making (IDM) technologies to solve voluminous, difficult-to-formalize tasks in a variety of subject areas, such as intelligent medical diagnostics of the human space, is of great importance [8]. Such problems, as a rule, are characterized by the absence or complexity of formal algorithms for solving [9], incomplete and unclear source information [10], [11], unclear goals achieved. These features lead to the need for use in the decision-making process of knowledge obtained from a human expert in the subject area, and the development of IDMs, which collect and manage this knowledge [8], [9], [11], decide on the optimal way to achieve goals in the conditions of incompleteness and fuzziness [6], [11].…”