Abstract: Number of The grouping information problem in its two basic manifestations recovering function, represented by empirical data (observations) and problem of classification (clusterization) and conception of its solving by the standard recurrent procedures are proposed and discussed. It is turn out that in both case correspond procedures can be designed on the base of so called neurofunctional transformations (NfT— transformations). Conception of such transformations implements the idea of superposition of standard functions by certain sequence of recurrent applications of the superposition. Least Square Method is used for designing the elementary functional transformations and implemented by necessary developed of M-P-inverse? technique. It is turn out that the same approach may be designed and implemented for solving the classification problem. Besides, the special classes of beam dynamics with delay were introduced and investigated to get classical results regarding gradients. These results were applied to optimize the NfT—transformations?.

Keywords: Grouping information problem, generalized artificial neuronets, learning samples, beam dynamics, Fuzzy likelihood equation, Multiset theory.

ACM Classification Keywords: G.2.m. Discrete mathematics: miscellaneous,G.2.1 Combinatorics. G.3 Probability and statistics, G.1.6. Numerical analysis I.5.1.Pattern Recognition H.1.m. Models and Principles: miscellaneous:

Link:

RECURRENT PROCEDURE IN SOLVING THE GROUPING INFORMATION PROBLEM IN APPLIED MATHEMATICS

V. Donchenko, Yu. Krivonos, Yu. Krak

http://www.foibg.com/ijima/vol01/ijima01-1-p06.pdf