Generating fuzzy rules from contradictory data of different control strategies and control performances

The design of fuzzy logic controllers for complex processes can be supported by the analysis of available observation data. Two main problems, however, arise in this context. First, in real world domains the data are contradictory. Second, the observation data can differ widely in quality, so that a uniform treatment or a simple classification into good and bad observation data leaves too much valuable information not being considered. In this paper, the fuzzy-ROSA method (Rule Orientated Statistic Analysis) is presented with a new concept for generating significant and quality-orientated fuzzy rules from observation data of different control strategies and control performances of the process under consideration. The method is illustrated by an application to an industrial six-axis robot arm