Name
Title | ||
|---|---|---|
Nonlinear Prediction Model Identification and Robust Prediction of Chaotic Time Series |
Abstract | ||
|---|---|---|
Although, in theory, the neural network is able to fit, model and predict any continuous determinant system, there is still an obstacle to prevent the neural network from wider and more effective applications due to the lack of complete theory of model identification. This paper addresses this issue by introducing a universal method to achieve nonlinear model identification. The proposed method is based on the theory of information entropy and its development, which is called as nonlinear irreducible autocorrelation. The latter is originally defined in the paper and could determine the optimal autoregressive order of nonlinear autoregression models by investigating the irreducible autodependency of the investigated time series. Following the above proposal, robust prediction of chaotic time series became realizable. Our idea is perfectly supported by computer simulations. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-28648-6_67 | ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 2 |
Keywords | Field | DocType |
computer simulation,time series,information entropy,neural network,prediction model,autoregressive model,model identification | Autoregressive model,Nonlinear system,Nonlinear autoregressive exogenous model,Computer science,Artificial intelligence,STAR model,System identification,Chaotic,Machine learning,Moving-average model,Autocorrelation | Conference |
Volume | ISSN | Citations |
3174 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuexian Hou | 1 | 269 | 38.59 |
| Weidi Dai | 2 | 13 | 2.96 |
| Pilian He | 3 | 29 | 7.46 |