Abstract
In this paper, we formulate nonlinear system identification problems in the case where output data is incomplete. Firstly, we propose an identification method based on an evolutionary algorithm, which is a fusion between a genetic algorithm (GA) and genetic programming (GP). Next, we give some descriptions on what GA and GP are like. Lastly under the situation when there is incomplete output data we illustrate the effectiveness of the proposed method through some simulations and through an experiment with a cart.
