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The optimizer bases of knowledge

The development of fuzzy controllers is one of the most practical applications of fuzzy systems. For developers of control systems, fuzzy systems are so attractive because of the fact they are universal approximator systems with poorly known dynamics and structure. In addition, they allow you to control dynamic object without human intervention.

Fuzzy controllers designed using soft calculations, have the following advantages: retain the main advantages of traditional management systems (stability, controllability, observability, etc.) are optimal (in terms of quality control test specified) base of knowledge, as well as the possibility of its correction and adaptation to the changing situation of management; ensure the attainability of the required quality control based on the engineered knowledge base can operate in contingency management

Increasing complexity of structures , objects of control and difficulties predicting unexpected (abnormal) situations only increase the urgency of control the problem and focus on finding the solution. Such problems belong to the problem System of Systems Engineering, studying in the general form complex structures of automated control systems with different levels and scales of integration and / or priority information exchange between subsystems in order to establish global (necessary and sufficient) conditions for safe operation of autonomous control object in the environment.

The optimizer bases of knowledge is based on soft calculations technologies, including the use of genetic algorithms to find optimal control and the use of fuzzy neural network to approximate the optimal control signal is found and retrieval based on his optimal knowledge.