A delay damage model selection algorithm for NARX neural networks

Recurrent neural networks have become popular models for system identiication and time series prediction. NARX (Nonlinear AutoRegressive models with eXogenous inputs) neural network models are a popular subclass of recurrent networks and have been used in many applications. Though embedded memory can be found in all recurrent network models, it is particularly prominent in NARX models. We show that using intelligent memory order selection through pruning and good initial heuristics signiicantly improves the generalization and predictive performance of these nonlinear systems on problems as diverse as grammatical inference and time series prediction.