Genetic optimization of fuzzy classification systems — A case study
Erich Peter Klement
|Title||Genetic optimization of fuzzy classification systems — A case study|
|Booktitle||Computational Intelligence in Theory and Practice|
|Series||Advances in Soft Computing Series|
This contribution presents a fuzzy method for a particular kind of pixel classification. It is one of the most important results of the development of an inspection system for a silk-screen printing process. The classification algorithm is applied to a reference image in the initial step of the printing process in order to obtain regions which are to be checked by applying different criteria. Tight limitations in terms of computation speed have necessitated very specific, efficient methods which operate locally. These methods are motivated and described in detail in the following. Furthermore, the optimization of the parameters of the classification system with genetic algorithms is discussed. Finally, the genetic approach is compared with other probabilistic optimization methods.