Multi-step-ahead optimal learning strategy for local model networks with higher degree polynomials

The idea of a learning strategy extension for nonlinear system identification with local polynomial model networks is presented in this paper. Usually the polynomial model tree (POLYMOT) algorithm utilizes a one-step-ahead optimal learning strategy. A demonstration example shows that this greedy behavior is not the best choice to reach a satisfying global model. Thus this strategy should be enlarged to a multi step-ahead optimal learning. Therefore, it is possible to find the optimal global model in a special case.