A neuro-fuzzy approach to system modelling. Part II. Applications

• Autorzy: Gorzałczany M. B.
• Języki publikacji: EN

Abstrakty

EN

The description of behaviour of complex and ill-defined systems and processes is usually based on a combination of two types of knowledge and data: a qualitative, fuzzy knowledge which contains elements of uncertainty and vagueness and often is expressed in the form of linguistic rules usually provided by a domain expert, and a quantitative, nonfuzzy information which appears in the form of measurements and other numerical data. Part I (ACS No. 1/2 1998) of this paper presents a methodology for modelling of complex systems which can effectively represent, process and generalize both mentioned-above types of system's knowledge. The proposed methodology combines artificial neural networks with some elements of the theory of fuzzy sets and fuzzy logic, yielding a structure that can be called a fuzzy neural network or a neuro-fuzzy system. Two examples of the application of our approach in the area of system modelling are presented in Part II of the paper.

Słowa kluczowe

EN

fuzzy sets; artificial neural networks; neuro-fuzzy system; fuzzy neural networks; fuzzy models; identification methods; fuzzy controllers

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 1998
• Tom: Vol. 7, no. 3/4
• Strony: 267--284
• Opis fizyczny: Bibliogr. poz. 14

Twórcy

autor

Gorzałczany M. B.

• Kielce University of Technology, Poland