Abstract
This paper is concerned with the use of radial basis function (RBF) neural networks aimed at an approximation of nonlinear mappings from R super(n) to R. The study is devoted to the design of these networks, especially their layer composed of RBF's, using the techniques of fuzzy clustering. Proposed is an idea of conditional clustering whose main objective is to develop clusters (receptive fields) preserving homogeneity of the clustered patterns with regard to their similarity in the input space as well as their respective values assumed in the output space. The detailed clustering algorithm is accompanied by extensive simulation studies.