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A neural network architecture for implementation of expert systems for real time monitoring
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Ramamoorthy, P. A. |
| Copyright Year | 1991 |
| Description | Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered. |
| File Size | 1679432 |
| Page Count | 44 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_19920005451 |
| Archival Resource Key | ark:/13960/t3xt0mn7m |
| Language | English |
| Publisher Date | 1991-09-25 |
| Access Restriction | Open |
| Subject Keyword | Cybernetics Domains Time Dependence Neural Nets Neurons Feedforward Control Massively Parallel Processors Expert Systems Parallel Processing Computers Fuzzy Systems Real Time Operation Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
