A DELAY DAMAGE MODEL SELECTION ALGORITHM FOR NARX NEURAL NETWORKS

Recurrent neural networks have become popular models for system identi fication and time series prediction, Nonlinear autoregressive models w ith exogenous inputs (NARX) neural network models are a popular subcla ss of recurrent networks and have been used in many applications, Alth ough embedded memory can be found in all recurrent network models, it is particularly prominent in NARX models. We show that using intellige nt memory order selection through pruning and good initial heuristics significantly improves the generalization and predictive performance o f these nonlinear systems on problems as diverse as grammatical infere nce and time series prediction.