Let a probability driven switch be defined as a switch of three input paths and three output paths. The status of the input paths defines a probability for each output path (as to whether it will generate a signal or not.) One output path is linked to one input path, so the results of the switch at time t can affect the switch at time t+1. A switch so constructed can be defined (by the probabilities) to take on the function of the standard logic gates (AND, OR, …) A net constructed of these switches can be “taught” by “reward” and “punish” algorithms to recognize input patterns. A simulation model showed that a repetitive learning algorithm coupled with a base knowledge (where new patterns are learned while continually checking past learned patterns) gives best results as a function of time. A good measure for the level of stability in response is to notice how many probabilities have converged to one or zero. The larger the number, the more stable the net.
| Identifer | oai:union.ndltd.org:pdx.edu/oai:pdxscholar.library.pdx.edu:open_access_etds-2999 |
| Date | 25 July 1974 |
| Creators | Carter, Lynn Robert |
| Publisher | PDXScholar |
| Source Sets | Portland State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations and Theses |
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