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On NIL : The software constructor of neural networks
Analog recurrent neural networks have attracted much attention lately as powerful tools of automatic learning. However, they are not as popular in industry as should be justified by their usefulness. The lack of any programming tool for networks, and their vague internal representation, leave the networks for the use of experts only. We propose a way to make the neural networks friendly to users by formally defining a high level language, called Neural Information Processing Programming Langage, which is rich enough to express any computer algorithm or rule-based system. We show how to compile a NIL program into a network which computes exactly as the original program and requires the same computation/convergence time and physical size. Allowing for a natural neural evolution after the construction, the neural networks are both capable of dynamical continuous learning and represent any given symbolic knowledge. Thus, the language along with its compiler may be thought of as the ultimate bridge from symbolic to analog computation.
World Scientific Publishing
ISSN : 0129-6264
Parallel processing letters A. 1996, vol. 6, n° 4, pp. 575-582 [bibl. : 18 ref.]
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