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Learning in artificial neural systems
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Matheus, Christopher J. Hohensee, William E. |
| Copyright Year | 1987 |
| Description | This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined. |
| File Size | 1653260 |
| Page Count | 28 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_19980040349 |
| Archival Resource Key | ark:/13960/t3b04214f |
| Language | English |
| Publisher Date | 1987-12-01 |
| Access Restriction | Open |
| Subject Keyword | Cybernetics Distributed Processing Algorithms Neural Nets Machine Learning Knowledge Representation Data Processing Architecture Computers Connection Machine Artificial Intelligence Expert Systems Parallel Processing Computers Belief Networks Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
