Automatic differential equations identification by self-configuring genetic programming algorithm
Tatiana Karaseva · IOP Conference Series Materials Science and Engineering · 2020
Abstract The paper considers a reduction of differential equations identification problem to the symbolic regression task. The current approach allows automatic determining the structure of a differential equation via the usage of the self-configuring genetic programming algorithm. The a priori information needed is only the dynamic system initial point and the sample of input and output effects. The stability of the proposed approach to the presence of noise in the sample and the small amount of data is investigated.