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Functional approximation using artificial neural networks in structural mechanics
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Alam, Javed Berke, Laszlo |
| Copyright Year | 1993 |
| Description | The artificial neural networks (ANN) methodology is an outgrowth of research in artificial intelligence. In this study, the feed-forward network model that was proposed by Rumelhart, Hinton, and Williams was applied to the mapping of functions that are encountered in structural mechanics problems. Several different network configurations were chosen to train the available data for problems in materials characterization and structural analysis of plates and shells. By using the recall process, the accuracy of these trained networks was assessed. |
| File Size | 802504 |
| Page Count | 20 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_19940006782 |
| Archival Resource Key | ark:/13960/t8jd9sf39 |
| Language | English |
| Publisher Date | 1993-07-01 |
| Access Restriction | Open |
| Subject Keyword | Structural Mechanics Neural Nets Plates Structural Members Shells Structural Forms Functional Analysis Artificial Intelligence Stress-strain Diagrams Structural Analysis Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Technical Report |
