ISSN 2071-8594

Russian academy of sciences

Editor-in-Chief

Gennady Osipov

V. G. Sinuk, S. V. Kulabukhov Comparative Analysis of the Inference Methods Based on the Fuzzy Truth Value for the MISO-Structure Systems

Abstract.

Fuzzy inference method based on fuzzy truth value enables us to perform fuzzy inference with polynomial computational complexity in case of multiple fuzzy inputs. This method also allows us to compare fuzzy implications against approximate reasoning of human intelligence. In this paper, several criteria for such comparison are proposed. First, the abovementioned fuzzy inference method is considered, the article provides its formal definition. Then the comparison criteria are defined in an appropriate form. The paper illustrates the principle of how different fuzzy implications can be tested against these criteria. As an example, Mamdani’s model with different t-norms has been examined. Finally, logical model with Godel and Rescher has been considered. In each case, fulfilled criteria are enumerated.

Keywords:

Fuzzy Truth Value, Performance Criteria, Fuzzy Inference Methods.

PP. 94-98.

DOI 10.14357/20718594200208

References

1. Kutsenko, D.A. and V.G. Sinuk. 2015. Metody vyvoda dlya system so mnogimi nechyotkimi vhodami [Inference method for systems with multiple fuzzy inputs]. Bulletin of the Russian Academy of Sciences: Control Theory and Systems. 54(3):375–383.
2. Mikhelev, V.V. and V.G. Sinuk. 2018. Metody vyvoda dlya system logicheskogo tipa na osnove nechyotkoy stepeni istinnosti [Inference Methods for Logical-Type Systems Based on Fuzzy Truth Degree]. Bulletin of the Russian Academy of Sciences: Control Theory and Systems. 57(3):108–115.
3. Mamdani, E.H. and S. Assilan. 1975. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies. 20(2):1–13.
4. Kudlacik, P. 2010. Advantages of Approximate Reasoning Based on a Fuzzy Truth Value. J. Medical Informatics & Technologies. 16:125–132.
5. Rutkowsky, L. 2010. Metody i tekhnologii iskusstvennogo intellekta [Methods and techniques of computational intelligence]. Moscow: Hot Line – Telecom. 520 p.
6. Fukami, S., M. Mizumoto and K. Tanaka. 1980. Some considerations of fuzzy conditional inference. Fuzzy sets and Systems. 4:243–273.
7. Aliev, R.A., A.E. Cerkovnyj and G.A. Mamedov. 1991. Upravlenie proizvodstvom pri nechetkoj iskhodnoj informacii [Production control with fuzzy source information]. Мoscow: Energoatomizdat. 239 p.
8. Zadeh, L.A. 1975. Fuzzy logic and approximate reasoning. Synthese. 30(3–4):407–428.
9. Dobuis, D. and H. Prade. 1990. Teoriya vozmozhnostey. Prilozheniye k predstavleniyu znaniy v informatike [Pos-sibility theory. Applications to the representation of knowledge in Informatics]. Moscow: Radio and Communication. 287 p.