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Higher-order Petri net models based on artificial neural networks

✍ Scribed by Tommy W.S. Chow; Jin-Yan Li

Publisher
Elsevier Science
Year
1997
Tongue
English
Weight
720 KB
Volume
92
Category
Article
ISSN
0004-3702

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✦ Synopsis

In this paper, the properties of higher-order neural networks are exploited in a new class of Petri nets, called higher-order Petri nets (HOPN). Using the similarities between neural networks and Petri nets this paper demonstrates how the McCullock-Pitts models and the higher-order neural networks can be represented by Petri nets. A 5-tuple HOPN is defined, a theorem on the relationship between the potential firability of the goal transition and the T-invariant (HOPN) is proved and discussed. The proposed HOPN can be applied to the polynomial clause subset of first-order predicate logic. A five-clause polynomial logic program example is also included to illustrate the theoretical results. 0 1997 Elsevier Science B.V.

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