A Study on Nonlinear Model Identi cation Using Pseudo-Bacterial Genetic Algorithm
چکیده
This paper presents a comparative study of the PseudoBacterial Genetic Algorithm (PBGA). The PBGA proposed by the authors is e cient in improving local portions of chromosomes. The PBGA has been considered to be efcient in the case where each locus in the chromosome has weak relationships with other loci. A rule base of a fuzzy inference system can be considered to be one of these cases. The weak relationships among fuzzy rules can be seen in the sense that the output is calculated using multiple rules, but each one of the rules still represents a unit with its own meaning and that can be locally improved. Numerical experiments are done to show the e ectiveness of the PBGA and also the conjunct e ect of the adaptive operator. The adaptive operator de nes the partitions for the bacterial operation and the crossover points according to the moving average of the truth values of the fuzzy rules. The results show the bene ts obtained with the combination of the PBGA with the adaptive operator.