Name
Abstract | ||
|---|---|---|
This paper develops an unscented grid-based filter for improved recurrent neural network modeling of time series. The filter approximates directly the weight posterior distribution as a linear mixture using deterministic unscented sampling. The weight posterior is obtained in one step, without linearisation through derivatives. An expectation maximisation algorithm is formulated for evaluation of the complete data likelihood and finding the state noise and observation noise hyperparemeters. Empirical investigations show that the proposed unscented grid filter compares favourably to other similar filters on recurrent network modeling of two real-world time series of environmental importance. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IJCNN.2010.5596830 | Neural Networks |
Keywords | Field | DocType |
expectation-maximisation algorithm,nonlinear filters,recurrent neural nets,statistical distributions,time series,Elman recurrent networks,data likelihood,deterministic unscented sampling,expectation maximisation algorithm,observation noise hyperparemeters,recurrent neural network modeling,sampling-based nonlinear filters,state noise,time series,unscented grid filtering,weight posterior distribution | Computer science,Control theory,Filter (signal processing),Recurrent neural network,Posterior probability,Unscented transform,Probability distribution,Sampling (statistics),Artificial intelligence,Machine learning,Network model,Grid | Conference |
ISSN | ISBN | Citations |
1098-7576 | 978-1-4244-6916-1 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikolay Y. Nikolaev | 1 | 57 | 6.46 |
| Derrick T. Mirikitani | 2 | 50 | 3.77 |
| Evgueni N. Smirnov | 3 | 24 | 20.38 |