You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
A directed graph processor and several optical realizations of its input symbolic feature vectors and the multi-processor operations required per node are given. This directed graph processor has advantages over tree and other hierarchical processors because of its large number of interconnections and its ability to adaptively add new nodes and restructure the graph. The use of the basic concepts of such a directed graph processor offer significant impact on: associative, symbolic, inference, feature space and correlation-based AI processors, as well as on knowledge base organization and procedural knowledge control of AI processors. Initial iconic alphanumeric data base results presented are most promising.
The alert did not successfully save. Please try again later.
Edward J. Baranoski, David Casasent, "Directed Graph Optical Processor," Proc. SPIE 0752, Digital Optical Computing, (11 August 1987); https://doi.org/10.1117/12.939910