Fuzzy system identification by generating and evolutionary optimizing fuzzy rule bases consisting of relevant fuzzy rules

One approach for system identification among many others is the fuzzy identification approach. The advantage of this approach compared to other analytical approaches is, that it is not necessary to make an assumption for the model to be used for the identification. In addition, the fuzzy approach can handle nonlinearities easier than analytical approaches. The Fuzzy–ROSA method is a method for data–based generation of fuzzy rules. This is the first step of a two step identification process. The second step is the optimization of the remaining free parameters, i.e. the composition of the rule base and the linguistic terms, to further improve the quality of the model and obtain small interpretable rule bases. In this paper, a new evolutionary strategy for the optimization of the linguistic terms of the output variable is presented. The effectiveness of the two step fuzzy identification is demonstrated on the benchmark problem ’kin dataset’ of the Delve dataset repository and the results are compared to analytical and neural network approaches.