Abai, MR

6th International Congress of DipterologyANN model performed well on the data set and unambiguously classifiedunknown samples.ANN classification of three different insect orders is possible and quitegeneral. It can be applicable for objects where appropriate database can becreated. After ANN “learning” (training) the species identification is fastand reliable. In contradiction to “manual” identification, all characters aresimultaneously taken into account over the complete database. Thisapproach is non-destructive unlike e.g. molecular analyses. Where theidentification appears difficult or it is e.g. sp.n., ANN can indicate thesituation. Study is supported by MSM 0021622416 and GACR524/05/H536 projects.Key Words: Artificial Neural Networks, insect identification, Tachinidae–Diptera, Thripidae–Thysanoptera, Phacopteronidae–Psylloidea∗∗∗∗∗∗∗∗∗∗290