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.
Department, Program, or Center
Department of Computing Security (GCCIS)
Wu, Wangming; Teh, Hoon-Heng; and Yuan, Bo, "Reasoning with propositional knowledge based on fuzzy neural logic" (1996). International Journal of Intelligent Systems - John Wiley & Sons, Inc, Vol. 11 (), pp. 251-265. Accessed from
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