NEURO-FUZZY CONTROL SYSTEM OF NON-DETERMINED ECONOMIC OBJECT
Abstract
The article discusses the use of neuro-fuzzy control systems as a tool for managing non-deterministic objects in real time. The application of classical methods description of the control system assumes that the control objects are described by linear dynamic links of low order. This assumption often leads to the fact that classical control systems in practice do not provide the specified indicators of fast and efficient management. This article discusses modern control tools structural models of a discrete quasi-invariant automated control system. Typical procedures correspond to business processes of nondeterministic discrete objects. The decision to use marketing, resource and production procedures is made on the basis of analysis of the degree of compliance; in this case, business processes in the economic object. This approach combines the advantages of the principle of using typical subsystems of automated control systems and the process approach. Presented in this article is the analysis of an automated control system, which is based on the use of typical models of discrete automated control systems. According to the proposed solution in an automated control system in real time, it is proposed to use a neuro-fuzzy control system as a function of the object and the system’s transfer ratio. The neuro-fuzzy control system is based on the process of learning an artificial neural network (ANN), which allows you to determine the rules of fuzzy inference (FIS). Once the fuzzy output parameters are defined, the neural network operates standard. In this integrated model, the neural network training algorithm (ANN) is used to determine the parameters of the fuzzy output system (FIS). On the other hand, the neural network learning mechanism does not depend on statistical information, but is standard for the chosen artificial neural network architecture. The ANFIS automated control system determines that each quantity is represented by only one fuzzy set. The ANFIS neural network learning procedure has no restrictions on modifying membership functions. To ensure the learning speed of the neural network and the adaptability of the software implementation, the model Takagi T., Sugeno M. this is based on a high-performance neural network learning procedure. The article proposes the ANFIS model, which considers an algorithm based on seven fuzzy rules.
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