ResearchBib Share Your Research, Maximize Your Social Impacts
Sign for Notice Everyday Sign up >> Login

Takagi-Sugeno Type Fuzzy Inference Module in the Case of Independent Linguistic Variables

Journal: International Journal of Mechanical and Production Engineering Research and Development (IJMPERD ) (Vol.10, No. 3)

Publication Date:

Authors : ; ;

Page : 12157-12166

Keywords : Fuzzy Set Theory; Neural Networks; Membership Function; Activation Function; Neural Network Calculations;

Source : Downloadexternal Find it from : Google Scholarexternal


The simultaneous identification of fuzzy rules and the adaptation of membership functions turned out to be very difficult or even impossible. This paper considers fuzzy neural networks that can solve this problem. We will discuss two types of fuzzy control modules that are based on the rules proposed by Takagi and Sugeno. These modules differ in the way they formulate conclusions of fuzzy rules. The result of the proposed approach will be expressed, firstly, in the implementation of the procedure for inference by the neural network of the corresponding structure and, secondly, in the display of the parameters of this procedure by the weights of connections.

Last modified: 2020-10-03 16:12:35