Cognitive Generator to Interpret Fuzzy Values
- 519 Downloads
The game approaches are rather popular in many applications, where a collective of automata is used. In the present paper the game involves a group of learning finite automata. The game is played sequentially with one automaton at a time, the result of the game defines next automaton to be played with. The goal of this game to provide some measuring system that is a reminiscent of collecting statistics in Probability Theory but in a different manner.
For measuring of an unknown membership value a new concept has been introduced called Cognitive Generator which transforms a fuzzy singleton to ordinary crisp logic value. Considerations on various types of axiomatic approaches show that the Cognitive Generator, as well as our Evidence Combination Axiomatic, belongs to one class of axiomatic theories, which may be used in applications directly.
Some programming examples aimed to illustrate our general approach.
KeywordsAxiomatic approach Cognitive generator Fuzzy value Fuzzy singleton T-norm Automata game Fuzzy value measuring Pseudorandom numbers PRNG PFS
- 3.Katke, S., Pawar, M., Kadam, T., Gonge, S.: Combination of neuro fuzzy techniques give better performance. Int. J. Comput. Sci. Inf. Technol. 6(1), 550–553 (2015)Google Scholar
- 4.Mishra, S., Hota, P.K., Mohanty, P.: A neuro-fuzzy based unified power flow controller for improvement of transient stability performance. CiteSeerGoogle Scholar
- 5.Mishra, S., Pradhan, A.K., Hota, P.K.: Development and implementation of a fuzzy logic based constant speed DC drive. J. Inst. Eng. (India), pt EL, 79, 146 (1998)Google Scholar
- 6.Senthil Kumar, M., Renuga, P., Saravanan, K.: Adaptive neuro-fuzzy based transient stability improvement using UPFC. Int. J. Recent Trends Eng. 2(7), 127 (2009)Google Scholar
- 7.Stefanuk, V.L.: Behavior of tsetlin’s learning automata in a fuzzy environment. In: Second World Conference on Soft Computing (WConSC), pp. 511–513. Letterpress, Baku, Azerbaijan (2012)Google Scholar
- 8.Stefanuk, V.L.: Interaction using qualitative data. In: 4th World Conference on Soft Computing. Program of Conference, Plenary Talk (Abstracts), pp. 43–44 (2014)Google Scholar
- 9.Vadim, S.L.: How to measure qualitative data. In: Proceedings of American Fuzzy Information Processing Society NAFIPS 2015 and 5th World Conference on Soft Computing, Redmond, USA, pp. 37–40 (2015)Google Scholar
- 10.Munakata, T.: Fundamentals of the New Artificial Intelligence, pp. 231. Springer, New York (1998)Google Scholar
- 11.Stefanuk, V.L.: Local organization of intellectual systems. Models and Applications, p. 328. Fizmatlit, Moscow (2004). (In Russian)Google Scholar
- 12.Stefanuk, V.L., Tzetlin, M.L.: On power control in the collective of radio stations. Inf. Transm. Probl. 3(4), 59–67 (1967)Google Scholar
- 13.Stefanuk, V.L.: Deterministic markovian chains. Inf. Process. 11(4), 702–709 (2011). IITP RAS, Moscow (in Russian)Google Scholar
- 14.Stefanuk, V.L.: Should one trust evidences? In: Proceedings of the All-Country AI Conference, Moscow, vol. 1, pp. 406–410 (1988)Google Scholar
- 15.Tsetlin, M.L.: Some problems of finite automata behaviour. Doklady USSR Acad. Sci. 139(4) (1961). MoscowGoogle Scholar
- 16.Stefanuk, V.L.: Processing of qualitative data. In: Proceedings of the First International Scientific Conference on Intelligent Information Technologies for Industry (IITI 2016), V.1. Advances in Intelligent Systems and Computing, vol. 456, pp. 373–379. Springer (2016)Google Scholar