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Abstract: In [8] we have shown how to construct a 3--layer recurrent neural network that computes the iteration of the meaning function TP of a given propositional logic program, what corresponds to the computation of the semantics of the program. In this article we define a notion of approximation for interpretations and prove that there exists a 3--layer feed forward neural network that approximates the calculation of TP for a given (first order) recurrent logic program with an injective level mapping... (Update)
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BibTeX entry: (Update)
Steffen H olldobler, Yvonne Kalinke, and Hans-Peter St orr. Approximating the semantics of logic programs by recurrent neural networks. Applied Intelligence, 11:45--58, 1999. http://citeseer.ist.psu.edu/olldobler99approximating.html More
@misc{ olldobler99approximating,
author = "S. olldobler and Y. Kalinke and H. orr",
title = "Approximating the semantics of logic programs by recurrent neural networks",
text = "Steffen H olldobler, Yvonne Kalinke, and Hans-Peter St orr. Approximating
the semantics of logic programs by recurrent neural networks. Applied Intelligence,
11:45--58, 1999.",
year = "1999",
url = "citeseer.ist.psu.edu/olldobler99approximating.html" }
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