AUTHORS: Anna Walaszek-Babiszewska

Download as PDF

ABSTRACT: In the paper the proposed dynamical model of the system is defined by fuzzy states and a transition function. The transition function is represented by conditional probabilities of respective fuzzy events. The criteria of optimization, as well as constraints, constitute fuzzy sets. The dynamical model has a form of a stochastic-fuzzy knowledge base, where the rules and weights of rules have been built by using large-scale data sets.

KEYWORDS: - Fuzzy control, Knowledge base, Stochastic system with fuzzy states

REFERENCES:

[1] Bellman R.E. and Zadeh L.A., Decision Making in a Fuzzy Environment, Management Science, 17, 1970, pp. 141-164.

[2] Kacprzyk J. and Staniewski P., A New Approach to the Control of Stochastic Systems in a Fuzzy Environment, Archiwum Automatyki i Telemechaniki, XXV, 1980, pp. 433-443.

[3] Kacprzyk J., Control of a Stochastic System in a Fuzzy Environment with Yager’s Probability of a Fuzzy Environment, Busefal, 12, 1982, pp. 77-88.

[4] Kacprzyk J., Multistage Decision Making under Fuzziness, Verlag, TUV Rheinland Cologne, 1983.

[5] Mamdani E.H. and Assilian S., An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller, International Journal of ManMachine Studies, 7, 1975, pp. 1-13.

[6] Tong R.M., Synthesis of Fuzzy Models for Industrial Processes – Some Recent Results, International Journal of General Systems, 4, 1978, pp. 143-163.

[7] Kacprzyk J., Multistage Fuzzy Control (in Polish), WNT Warsaw, 2001.

[8] Zadeh L.A., Fuzzy Sets, Information and Control, 8, 1965, pp. 338-353.

[9] Walaszek-Babiszewska A., Fuzzy Modeling in Stochastic Environment. Theory, Knowledge Bases, Examples, LAP LAMBERT Academic Publishing, Saarbrucken, 2011.

[10] Walaszek-Babiszewska A. and Rudnik K., Stochastic-Fuzzy Knowledge-Based Approach to Temporal Data Modeling, in: Time Series Analysis, Modeling and Applications, pp. 97- 118, Pedrycz W. and Shyi-Ming Chen (Eds), Springer, Berlin 2013.