Localization of Sound Sources by Means of Recurrent Neural Networks
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Prediction of chaotic time series with NARX recurrent dynamic neural networks
ICAI'08 Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information
The use of NARX neural networks to predict chaotic time series
WSEAS Transactions on Computer Research
IEEE Transactions on Neural Networks
Chaotic model with data assimilation using NARX network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
| Hi-index | 35.68 |
Recurrent neural networks have become popular models for system identification and time series prediction. Nonlinear autoregressive models with exogenous inputs (NARX) neural network models are a popular subclass of recurrent networks and have been used in many applications. Although 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 significantly improves the generalization and predictive performance of these nonlinear systems on problems as diverse as grammatical inference and time series prediction