Name
Abstract | ||
|---|---|---|
BP neural network and multiple linear regression model can be used for multi-factor analysis and forecasting, but the data of the multiple linear regression required to meet independence, normality and other conditions, while the data of the BP neural network do not need to. This article uses the same set of data to established BP neural network model and multiple linear regression model, then compare the ability of fitting and forecasting of the two kinds of models finding that BP neural network has a strong fitting ability and a stable ability of prediction, which can be further used and promoted in the anglicizing and forecasting of the continuous data factors. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16167-4_47 | ICICA (LNCS) |
Keywords | Field | DocType |
multi-factor analysis,stable ability,multiple linear regression,multiple linear regression model,continuous data factor,established bp neural network,bp neural network,strong fitting ability,multiple linear regression method,neural network model,neural network,factor analysis,forecast,coefficient of determination | Normality,Data mining,Computer science,Probabilistic neural network,Coefficient of determination,Artificial neural network,Multiple linear regression model,Linear regression | Conference |
Volume | ISSN | ISBN |
6377 | 0302-9743 | 3-642-16166-9 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoli Wang | 1 | 2 | 2.41 |
| Jianhui Wu | 2 | 1 | 1.37 |
| Sufeng Yin | 3 | 0 | 1.35 |
| Liqun Yu | 4 | 0 | 0.68 |
| Jing Wang | 5 | 277 | 39.00 |