Name
Abstract | ||
|---|---|---|
A PCA-BPNN model is proposed and simulated which aims to resolve the difficulties faced by the product quality prediction in the modern industry which is brought by the high dimension of production parameters generated in the complex and nonlinear production process. The PCA algorithm is introduced into the BPNN model to realize the dimension reduction without vital information loss which can simplify the architecture of the neural network. The PCA-BPNN model is illustrated and simulated. Experimental results show that this model is superior over the BPNN which achieves a stable prediction performance with a rapid convergence and can overcome the problem, oscillation of MSE, occurred in the BPNN. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3233/978-1-61499-722-1-390 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
quality prediction,high dimension,PCA,BPNN | Data mining,Computer science,Manufacturing process | Conference |
Volume | ISSN | Citations |
293 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Zhou | 1 | 27 | 14.24 |
| Kun-Ming Yu | 2 | 258 | 27.07 |