Incremental local model networks for time series prediction

Radial basis function networks and local model networks have been successfully used for system modelling and prediction. One issue concerning these techniques, not yet satisfactorily solved, is the location of the basis functions in input space. In the paper, a novel modular architecture, which combines a growing neural gas network with local model networks, is proposed. In the construction of the local model networks, the topological neighborhood defined among the growing neural gas network units, is employed. The proposed method was used to make short term predictions of different computer generated time series in the presence of noise. The experimental results have shown that the proposed neural network can be effectively used for chaotic time series prediction achieving performances at least comparable to state-of-the-art methods