Karakalpak Scientific Journal


The Results of interaction of the work is development of a method of control that allows simplifying, automating and unifying the process of design of the hybrid systems which are a basis of modern automation. To achieve a definite purpose, a method of control of a technical object based on the construction of an adaptive system of neuro-fuzzy inference is developed. The objects of the system of neuro-fuzzy inference are the classical and neuro-fuzzy models of control. Information exchange between models is provided by means of the developed hybrid control system. The result of the interaction of the two models is the automatic formation of the base of fuzzy controller rules based on knowledge about the control object obtained with its control using the classical controller. In the developed adaptive system of neuro-fuzzy inference signals of error and control in the classical model are used as data for creation a hybrid network. Signals of error and control in a neuro-fuzzy mod el with automatically generated neuro-fuzzy inference rules are used as data to verify the created hybrid network in order to detect the fact of its retraining. Thus, during the control of a technical object by means of a hybrid system, the knowledge of an expert in the subject area for adjusting the parameters of the fuzzy controller are completely eliminated, that allows to control difficultly formalizable objects in conditions of uncertainty. To obtain reliable research results, a hybrid control system, consisting of classical and neuro-fuzzy models is developed. Numerical values of the error and control signals are obtained at discrete time points as a result of the interaction of the two models. Special files for creating and testing a hybrid network in the form of numerical matrixes are generated. The hybrid network is developed in the ANFIS editor of the MATLAB package. The generated structure of the FIS fuzzy inference system of the Sugeno type is graphically shown. The visualization of the dependence of training and verification errors from the number of training cycles is given. The surface of the fuzzy inference system is constructed, that allows estimating the dependence of the output variable on the input.

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