An evolutionary approach to identification problems with incomplete output data
Joe Imae, Yasuhiko Morita, Guisheng Zhai, Tomoaki Kobayashi · 2008
In this paper, we consider nonlinear system identification problems in the case where output data is incomplete. We propose an identification method based on an evolutionary algorithm, which is a fusion of a genetic algorithm (GA) and genetic programming (GP), and illustrate the effectiveness of the proposed method through a simulation and an experiment with a cart.