Name
Abstract | ||
|---|---|---|
Thermal deformation is a nonlinear dynamic phenomenon and is one of the significant factors for the accuracy of machine tools. In this study, a dynamic feed-forward neural network model is built to predict the thermal deformation of machine tool. The temperatures and thermal deformations data at present and past sampling time interval are used train the proposed neural model. Thus, it can model dynamic and the nonlinear relationship between input and output data pairs. According to the comparison results, the proposed neural model can obtain better predictive accuracy than that of some other neural model. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11893257_94 | ICONIP |
Keywords | Field | DocType |
thermal deformation prediction,thermal deformations data,thermal deformation,predictive accuracy,machine tool,proposed neural model,neural model,output data pair,nonlinear relationship,nonlinear dynamic phenomenon,dynamic feed-forward neural network,prediction model,feed forward neural network,neural network,nonlinear dynamics | Nonlinear system,Computer science,Input/output,Artificial intelligence,Artificial neural network,Machine tool,Feedforward neural network,Thermal,Pattern recognition,Simulation,Algorithm,Stress (mechanics),Sampling (statistics) | Conference |
Volume | ISSN | ISBN |
4233 | 0302-9743 | 3-540-46481-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuan-Wei Chang | 1 | 5 | 1.69 |
| Yuan Kang | 2 | 25 | 7.42 |
| Yi-Wei Chen | 3 | 0 | 0.34 |
| Ming-Hui Chu | 4 | 8 | 3.13 |
| Yea-Ping Wang | 5 | 0 | 0.68 |