Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2 2003

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: fuzzy systems of type 2

Sortuj według:

Ogranicz wyniki do:

Fuzzy inference neural networks with fuzzy parameters

Rutkowska D., Hayashi Y.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2003

Vol. 7, No 1

7-22

This paper concerns fuzzy neural networks and fuzzy inference neural networks, which are two different approaches to neuro-fuzzy combinations. The former is a direct fuzzification of artificial neural networks by introducing fuzzy signals and fuzzy weights. The latter is a representation of fuzzy systems in the form of multi-layer connectionist networks, similar to neural networks. Parameters of membership functions (centers and widths) play the role of neural network weights. In this paper, fuzzy inference neural networks with fuzzy parameters are considered. Neuro-fuzzy systems of this kind utilize both approaches: fuzzy neural networks and fuzzy inference neural networks. They also pertain to fuzzy systems of type 2 since membership functions with fuzzy parameters characterize type 2 fuzzy sets. Various architectures of these networks have been obtained for fuzzy systems based on different fuzzy implications. By analogy with fuzzy inference neural networks with crisp parameters, methods of learning fuzzy parameters and rule generation can be derived for neuro-fuzzy systems with fuzzy parameters. Fuzzy inference neural networks are studied in the framework of fuzzy granulation. In particular, fuzzy clustering as fuzzy information granulation is proposed to be applied in order to generate fuzzy IF-THEN rules. Applications of fuzzy inference neural networks are also outlined.

A constructive approach to managing fuzzy subsets of type 2 in a decision making

Dymova L.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2003

Vol. 7, No 1

157-164

The aim of this paper is to present a constructive methodology and algorithms for operations with fuzzy sets of type 2. The need to elaborate this methodology came from practical problems of Decision Making. To realize the methodology, some simplifications of the problem have been introduced. Particularly, only the trapezium form of membership functions was used. To highlight the difference between the proposed approach and the classical theory of fuzzy sets of type 2, the terms "hyperfuzzy set" and "hyperfuzzy function" have been introduced. Some base situations of hyperfuzzy functions with real arguments and real functions of hyperfuzzy arguments are performed.