Algorithms for inductive generation of superpositions for approximation of experimental data

The paper presents an algorithm which inductively generates admissible nonlinear models. An algorithm to generate all admissible superpositions of given complexity in finite number of iterations is proposed. The proof of its correctness is stated. The proposed approach is illustrated by a computational experiment on synthetic data.