Synthesis Of An Adaptive Identifier For A Neural Fuzzy Control System Under Uncertainty

Abstract

In the article an adaptive identifier is proposed for a neuro-fuzzy control system of a nonlinear dynamic object operating under conditions of uncertainty of internal properties and the external environment. Algorithms of real-time structural and parametric identification have been developed, which is a combination of an algorithm for identifying linear control coefficients and a method of interactive adaptation theory. The developed hybrid model, built on the basis of neural networks and fuzzy models, makes it possible to increase the efficiency of solving the problem of managing complex dynamic objects in conditions of uncertainty