Name
Title | ||
|---|---|---|
Application of neural networks for the prediction of cartilage stress in a musculoskeletal system. |
Abstract | ||
|---|---|---|
•The finite element method is computationally intensive, rigid body models cannot predict stress.•Neural network models are created to predict stress from multibody reaction forces.•Neural networks learn from finite element and multibody model solutions.•End goal: musculoskeletal models that simultaneously predict muscle force and cartilage stress. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.bspc.2013.04.004 | Biomedical Signal Processing and Control |
Keywords | Field | DocType |
Finite element analysis,Musculoskeletal simulation,Neural networks,Cartilage stress | Boundary value problem,Telecommunications,Nonlinear system,Pattern recognition,Control theory,Computer science,Finite element method,Musculoskeletal movement,Artificial intelligence,Artificial neural network,Musculoskeletal tissue | Journal |
Volume | Issue | ISSN |
8 | 6 | 1746-8094 |
Citations | PageRank | References |
4 | 0.75 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunkai Lu | 1 | 4 | 0.75 |
| Palgun Reddy Pulasani | 2 | 4 | 0.75 |
| Reza Derakhshani | 3 | 166 | 21.08 |
| Trent M. Guess | 4 | 9 | 2.37 |