Reasoning with propositional knowledge based on fuzzy neural logic

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Title: Reasoning with propositional knowledge based on fuzzy neural logic
Author: Wu, Wangming; Teh, Hoon-Heng; Yuan, Bo
Abstract: 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.
Description: Copyright 1996 John Wiley & Sons, Inc. Article from the International Journal of Intelligent Systems.
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Date: 1996

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