INFORMATION-EXTREME ALGORITHM OF
UNIMODAL CLASSIFIER FOR GROWING SINGLE CRYSTAL DIAMETER EVALUATION
A.S. Dovbish, V.V. Moskalenko
This paper proposes information-extreme
algorithm of analis and synthesis of learning and
self-learning decision support system (DSS). It based on unimodal
classifier characterized by single center of pattern vector-realizations distribution
and structured alphabet of recognition classes. Training the DSS optimizes coltrol permits vector for identification signs by the
simultaneous changing the parameter of coltrol permits
field for all the signs.
As a criterion of the
functional efficiency of training was considered modified normalized Kullback information criterion. Training parameters
optimization carries iterative search for the global maximum Kullback criterion in the allowable domain of it function
definition. On the example of DSS for control of growing scintillate single
crystals, the article considers implementation the algorithm. According results
of physical modeling unimodal classifier shown
increase DSS training functional efficiency compared with multimodal
classifier.
Keywords: optimization, training, unimodal
classifier, decision support system, management, scintillate single crystal.