Structural-Parametric Synthesis of an Adaptive Fuzzy-Logical System

Siddikov Isamiddin Xakimovich,Yadgarova Dilnoza Baxtiyarovna,Umurzakova Dilnoza Maxamadjonovna

doi:10.13189/ujeee.2020.070204

Siddikov Isamiddin Xakimovich, Yadgarova Dilnoza Baxtiyarovna + Show 1 more

Open Access

https://doi.org/10.13189/ujeee.2020.070204

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Abstract

One of the ways to improve the quality of the process of managing dynamic objects, in the presence of various types of uncertainty of external and internal factors, due to insufficient information about the object and useful signals and noise acting on it, is to develop new and improve existing methods and algorithms for solving problems of structurally parametric synthesis of neuro-fuzzy control systems that can significantly expand the capabilities of dynamic systems. An analysis of existing approaches showed that at present, traditional methods of controlling the absorption process do not meet modern requirements, due to the lack of a system analysis of the entire set of control systems as a whole, the structure of the system and the relationship between its functional elements, etc. The aim of the work is to create an automatic control system dynamic object, allowing to overcome the difficulties associated with the non-stationary process, structural and parametric uncertainty and variability of external influences. A technique for the synthesis of a high-speed control algorithm based on fuzzy-logical inference is proposed, which allows to eliminate empty and zero solutions when determining the architecture and calculating the weights of arcs of a neural network. To correct the parameters and structure of the fuzzy-logical controller, an adaptation block is proposed in the control system loop. The originality of the proposed synthesis method is to ensure high speed of finding control actions due to the possibility of eliminating the redundancy of computational procedures associated with discarding empty and zero solutions in the formation of control devices when choosing the architecture of a neural network and calculating synapse weights. The proposed structural-parametric adaptation algorithm in the process control problems allows to reduce the number of iterations in the process of network training, to reduce the error in the calculation of control values 8 to 1%.

Highlights

Control theory pays special attention to the synthesis of mathematical models and control algorithms with insufficient information about the control object and useful signals and noise acting on it
The most promising direction in solving this problem is the use of universal approximators of a wide class of multidimensional nonlinear functions - adaptive models of fuzzy inference and adaptive fuzzy neural networks
The results showed that after 10 iterations, the accuracy of training is about 5 %, it is necessary to carry out the correction of one parameter

Summary

Introduction

Control theory pays special attention to the synthesis of mathematical models and control algorithms with insufficient information about the control object and useful signals and noise acting on it This interest has intensified recently in connection with the study of weakly formalized complex systems and the development of principles and algorithms for controlling these systems. The parameters of adaptive models of both types are tuned by optimizing them in the sense of a certain criterion formed from data from the training sample. Solving this optimization problem is a difficult task for a number of reasons. Gradient optimization methods are standard and well-studied This group of methods uses the value of the gradient of the function to search for the extremum. The disadvantage of these methods is the need to calculate the gradient of the function, the dependence on the initial approximation

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