Abstract
A multilayer neural network development environment, called ANNDE, is presented for implementing effective learning algorithms for the domain of engineering design using the object-oriented programming paradigm. It consists of five primary components: learning domain, neural nets, library of learning strategies, learning process, and analysis process. These components have been implemented as five classes in two object-oriented programming languages C++ and G++. The library of learning strategies includes generalized delta rule with error backpropagation. Several examples are presented for learning in the domain of structural engineering.
| Original language | English |
|---|---|
| Pages (from-to) | 45-55 |
| Number of pages | 11 |
| Journal | Neurocomputing |
| Volume | 6 |
| Issue number | 1 |
| DOIs | |
| State | Published - Feb 1994 |
Keywords
- Backpropagation
- object-oriented programming
- structural design