Abstract: In the paper certain approach to the selection of identification parameters of relational fuzzy cognitive map's dynamic model, based on appropriate computational algorithms of identification, is described. On the selected simulation results there is presented the comparison of the efficiency of different methods of parameters selection (adaptation) in fuzzy relations depending on the adaptation assumed goal.
B I B L I O G R A F I A[1] Jastriebow A.: Synthesis and analysis of m-step adaptation algorithms of the parametric identification of static and dynamic objects on the base of discrete measurement. Zeszyty Naukowe Politechniki Świętokrzyskiej, Wyd. PŚk, Kielce 1990 (in Polish).
[2] Jastriebow A., Słoń G.: Granular calculations in modeling of imprecise object using fuzzy relational cognitive maps. Pomiary, Automatyka, Kontrola vol. 56 nr 12/2010, pp. 1449-1452 (in Polish).
[3] Jastriebow A., Wciślik W.: Optimization - theory, algorithms and their executing in MATLAB. Skrypt nr 401 Politechniki Świętokrzyskiej, Wyd. PŚk, Kielce 2004 (in Polish).
[4] Papageorgiou E. I., Stylios C. D.: Fuzzy Cognitive Maps, In: Pedrycz W., Skowron A., Kreinovich V.: Handbook of Granular Computing, Chichester, England, John Wiley & Son Ltd, Publication Atrium 2008, pp. 755-775.
[5] Pedrycz W.: Fuzzy Sets as a User-Centric Processing of Granular Computing, In: Pedrycz W., Skowron A., Kreinovich V.: Handbook of Granular Computing, Chichester, England, John Wiley & Son Ltd, Publication Atrium 2008.
[6] Słoń G.: The analysis of selected algorithms of the adaptation of relations in fuzzy cognitive maps. Pomiary, Automatyka, Kontrola vol. 56 nr 12/2010, pp. 1445-1448 (in Polish).
[7] Stach W., Kurgan L., Pedrycz W., Reformat M., Genetic Learning of Fuzzy Cognitive Maps, Fuzzy Sets and Systems, 2005, V. 153, pp. 371-401. 
YADDA/CEON