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Learning control of inverted pendulum system by neural network driven fuzzy reasoning: the learning function of nn-driven fuzzy reasoning under changes of reasoning environment
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Wakami, Noboru Nomura, Hiroyoshi Hayashi, Isao |
| Copyright Year | 1991 |
| Description | Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm. |
| File Size | 772074 |
| Page Count | 14 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_19910012472 |
| Archival Resource Key | ark:/13960/t8dg1nk69 |
| Language | English |
| Publisher Date | 1991-02-01 |
| Access Restriction | Open |
| Subject Keyword | Cybernetics Controllers Algorithms Neural Nets Control Systems Design Pendulums Inference Artificial Intelligence Rules Tuning Fuzzy Systems Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
