Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10324194 | Fuzzy Sets and Systems | 2005 | 20 Pages |
Abstract
The nonlinear autoregressive moving average with exogenous inputs (NARMAX) model provides a powerful representation for time series analysis, modeling and prediction due to its capability of accommodating the dynamic, complex and nonlinear nature of real-world time series prediction problems. This paper focuses on the modeling and prediction of NARMAX-model-based time series using the fuzzy neural network (FNN) methodology. Both feedforward and recurrent FNNs approaches are proposed. Furthermore, an efficient algorithm for model structure determination and parameter identification with the aim of producing improved predictive performance for NARMAX time-series models is developed. Experiments and comparative studies demonstrate that the proposed FNN approaches can effectively learn complex temporal sequences in an adaptive way and they outperform some well-known existing methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yang Gao, Meng Joo Er,