Name
Title | ||
|---|---|---|
Neural network modeling of the cellgap process for liquid crystal display fabricated on plastic substrates |
Abstract | ||
|---|---|---|
In this paper, a neural network model is presented to characterize the thickness and the uniformity of the cellgap process for flexible liquid crystal display (LCD). Input factors are explored via a D-optimal design with 15 runs and used as training data in the neural network. In order to verify the fitness of the model, three more runs are added as test data. Latin hypercube sampling and error back-propagation algorithm are used to build the model. Latin hypercube sampling is used to generate initial weights and biases of the network. The thickness of cellgap is measured at five points: one at the center and four at the edges. The average thickness is used as cellgap thickness, and the uniformity is obtained by comparing the thickness at the center and edge points. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.eswa.2007.08.027 | Expert Syst. Appl. |
Keywords | Field | DocType |
cellgap,average thickness,liquid crystal display,neural network,neural networks,neural network modeling,test data,edge point,d-optimal design,neural network model,latin hypercube sampling,training data,cellgap process,plastic substrate,cellgap thickness,flexible lcd,d optimal design,error back propagation | Training set,Data mining,Simulation,Computer science,Algorithm,Liquid-crystal display,Test data,Artificial neural network,Neural network modeling,Latin hypercube sampling,Flexible display | Journal |
Volume | Issue | ISSN |
35 | 3 | Expert Systems With Applications |
Citations | PageRank | References |
1 | 0.45 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jung Hwan Lee | 1 | 3 | 1.59 |
| Donghun Kang | 2 | 10 | 3.55 |
| Young-Don Ko | 3 | 12 | 4.16 |
| Jaejin Jang | 4 | 6 | 1.98 |
| Dae-Shik Seo | 5 | 1 | 0.45 |
| Ilgu Yun | 6 | 25 | 12.28 |