Modeling of multi-factory dependences in complex control systems by SUGENO fuzzy knowledge base

Abstract

The creation of intelligent control systems based on soft computing for complex real-time systems is considered. Usually, the control of many objects and technological processes is performed by a human operator based on instructions and personal experience. Due to the uncertainty and incompleteness of information about the parameters of the object, the impossibility of their direct measurement and the natural diversity of the environment, the use of classical mathematical methods for the automation of control is impossible. Adaptive methods are used to solve the management problem under conditions of uncertainty. The most effective is the implementation of fuzzy control based on production rules, which does not require knowledge of the object model. The concept of fuzzy control is widely used because of its ability to operate according to conditions. The object is evaluated using fuzzy rules from the knowledge base that create a fuzzy decision domain. The stage of structural identification of the system when modeling an unknown dependency by Sugeno's fuzzy knowledge base is presented, which allows implementing a control algorithm based on a neural network. To take into account the maximum number of influencing factors, the hierarchical structure of the fuzzy control system is applied. Granulation by certain properties of the object and the environment at the level of input variables allows to reduce the dimensionality of the data vector and the number of rules of the knowledge base, making it possible to build fuzzy control systems with an unlimited number of inputs. According to this model, the decision-making process is divided into two levels. The first level reduces the amount of information to define the situations to be processed in the second level by the Sugeno knowledge base. A preliminary fuzzy model of control of complex dynamic objects with incomplete determination is built.