InterCriteria Analysis of a model parameters identification using genetic algorithm

In this paper we apply an approach based on the apparatus of the Index Matrices and the Intuitionistic Fuzzy Sets - namely InterCriteria Analysis. The main idea is to use the InterCriteria Analysis to establish the existing relations and dependencies of defined parameters in non-linear model of an E. coli fed-batch cultivation process. Moreover, based on results of series of identification procedures we observe the mutual relations between model parameters and considered optimization techniques outcomes, such as execution time and objective function value. Based on InterCriteria Analysis we examine the obtained identification results and discuss the conclusions about existing relations and dependencies between defined, in terms of InterCriteria Analysis, criteria.