АНАЛИЗ МОДЕЛЕЙ АДАПТИВНЫХ НЕЙРО-НЕЧЕТКИХ СИСТЕМ

Abstract

The article deals with the study of basic methods for models of adaptive neuro-fuzzy systems. Based on the analysis, the strengths of neural networks and fuzzy logic were found, that became powerful tools for solving complex modeling and forecasting issues. There is studying and analyzing the adaptive neural network, which is a class of neural networks that have the ability to change their structure and parameters in the process of learning and adaptation to new data and conditions and besides the article studies the Gaussian membership function, also known as the normal membership function or the Gauss-type membership function, which is a valuable tool in the f ield of fuzzy logic and fuzzy systems. The paper provides as well an analysis of the generalized Bell membership function, also known as the Bell type membership function or Bell function, which plays an important role in the field of fuzzy logic and fuzzy systems. Furthermore it analyzes the Tsukamoto model, which is one of the main models of fuzzy logic. The author opted to choose the Co-Active Neuro-Fuzzy Inference System model, which is an adaptive neuro-fuzzy system that combines neural networks and fuzzy logic for processing data with uncertainty and fuzziness. With the further implementation of the combined model based on the abovelisted models based on the STL of the C++ language, thus the neural network model is obtained, the model with versatility, that is achieved by using a combination of those models. That will facilitate its easy modification and adaptation to various tasks.