Learning Strategies for Local Model Networks with Higher Degree Polynomials

AbstractIn this paper a new algorithm for nonlinear system identification with local models of higher polynomial degree is proposed. Usually the local models are linearly parameterized and those parameters are typically estimated by some least squares approach. For the utilization of higher degree polynomials this procedure is no longer feasible due to the exponentially increasing number of parameters depending on a cumulative number of physical inputs. Thus a new learning strategy with the aid of stepwise regression is developed to estimate only the most significant parameters. The included partitioning algorithm decides in each step between increasing the number of parameters of the worst local model and splitting this model to create two new ones. Its advantages are illustrated by a demonstration example.