A neuro-fuzzy approach to system modelling. Part II. Applications
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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.
Bibliogr. poz. 14