A novel machine learning algorithm and its use in the modelling and simulation of dynamical systems

Author

Zografski, Zlatko

Author_Institution

Fac. of Electr. Eng., Univ. Kiril i Metodij, Skopje, Yugoslavia

fYear

1991

fDate

13-16 May 1991

Firstpage

860

Lastpage

864

Abstract

A machine learning method, suitable for applications in domains involving complex nonlinear systems, is presented. The learning algorithm is used to construct a kind of associative memory which features a sophisticated local interpolation scheme and fast searching algorithms. Experiments with the implemented algorithm in the acquisition of the dynamics model for the well-known pole-balancing system verify the algorithm´s theoretically derived time and space requirements and demonstrate its efficiency

Keywords

computational complexity; content-addressable storage; interpolation; large-scale systems; learning systems; nonlinear control systems; search problems; complex nonlinear systems; dynamical systems; dynamics model; fast searching algorithms; local interpolation scheme; machine learning algorithm; pole-balancing; space complexity; space requirements; time complexity; Artificial intelligence; Associative memory; Interpolation; Learning systems; Machine learning; Machine learning algorithms; Multidimensional systems; Nonlinear dynamical systems; Nonlinear systems; Process control;

fLanguage

English

Publisher

ieee

Conference_Titel

CompEuro '91. Advanced Computer Technology, Reliable Systems and Applications. 5th Annual European Computer Conference. Proceedings.

Conference_Location

Bologna

Print_ISBN

0-8186-2141-9

Type

conf

DOI

10.1109/CMPEUR.1991.257504

Filename

257504