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Reasoning with propositional knowledge based on fuzzy neural logic

✍ Scribed by Wangming Wu; Hoon-Heng Teh; Bo Yuan

Publisher: John Wiley and Sons
Year: 1998
Tongue: English
Weight: 760 KB
Volume: 11
Category: Article
ISSN: 0884-8173
DOI: 10.1002/(sici)1098-111x(199605)11:5<251::aid-int1>3.0.co;2-n

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✦ Synopsis

In this article, a new kind of reasoning for propositional knowledge, which is based on the fuzzy neural logic initialed by Teh, is introduced. A fundamental theorem is presented showing that any fuzzy neural logic network can be represented by operations: bounded sum, complement, and scalar product. Propositional calculus of fuzzy neural logic is also investigated. Linear programming problems risen from the propositional calculus of fuzzy neural logic show a great advantage in applying fuzzy neural logic to answer imprecise questions in knowledge-based systems. An example is reconsidered here to illustrate the theory.

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