The Extraction of Elliptical Rules from the Trained Radial Basis Function Neural Network

2012
Andrejs Bondarenko, Arkādijs Borisovs

The paper describes an algorithm for approximation of trained radial basis function neural network (RBFNN) classification boundary with the help of elliptic rules. These rules can later be translated into IF-THEN form if required. We provide experimental results of the algorithm for a two-dimensional case. Currently, neural networks are not widely used and spread due to difficulties with the interpretation of classification decision being made. The formalized representation of decision process is required in many mission critical areas, such as medicine, nuclear energy, finance and others.

Keywords
radial basis function networks, knowledge acquisition, optimization

Bondarenko, A., Borisovs, A. The Extraction of Elliptical Rules from the Trained Radial Basis Function Neural Network. Information Technology and Management Science. Vol.15, 2012, pp.161-165. ISSN 2255-9086. e-ISSN 2255-9094.

Publication language
English (en)