Name
Abstract | ||
|---|---|---|
A fuzzy neural network (FNN) multi-step prediction model based on singular spectrum analysis (SSA) and mean generating function (MGF) for summer precipitation has been developed in this paper. In the modeling process, the original standardized sample series of summer precipitation was denoised and reconstructed with SSA, the extended matrix of MGF of the reconstructed precipitation series (as the input factor) and the original standardized sample series (as the output factor) were then used to develop a three-layer FNN multi-step prediction model for summer precipitation. Results show that the SSA-MGF FNN model is superior to the other three models in prediction accuracy. This indicates that denoising of SSA and FNN prediction model are relatively effective for raising the accuracy of precipitation prediction, and the SSA-MGF FNN multi-step prediction model proposed in this paper is of application value. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CSO.2009.107 | CSO (2) |
Keywords | Field | DocType |
climatology,signal denoising,original standardized sample series,mean generating function,ssa-mgf fnn model,prediction accuracy,signal sampling,ssa-mgf fnn multi-step prediction,summer precipitation,summer precipitation fnn multi-step,matrix algebra,reconstructed precipitation series,spectral analysis,multi-step prediction model,fnn prediction model,atmospheric precipitation,geophysical signal processing,three-layer fnn multi-step prediction,signal reconstruction,summer precipitation fnn,precipitation prediction,standardized sample series,prediction model,ssa-mgf,singular spectrum analysis,multistep prediction model,fuzzy neural nets,fuzzy neural network,noise reduction,computational modeling,atmospheric modeling,accuracy,mathematical model,artificial neural networks,neural networks,computer networks,predictive models,generating function,meteorology,information processing | Noise reduction,Generating function,Computer science,Matrix (mathematics),Atmospheric model,Artificial intelligence,Singular spectrum analysis,Artificial neural network,Machine learning,Signal reconstruction,Precipitation | Conference |
Volume | ISBN | Citations |
2 | 978-0-7695-3605-7 | 0 |
PageRank | References | Authors |
0.34 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong-hua Li | 1 | 0 | 0.34 |
| Hai-ming Xu | 2 | 0 | 1.35 |
| Yang-hua Gao | 3 | 0 | 1.01 |
| Suo-quan Zhou | 4 | 0 | 0.34 |
| Qiang Li | 5 | 15 | 1.84 |