Design of function-generating mapping networks by interactive neural-network simulation

Granino A. Korn

Mathematics and Computers in Simulation (MATCOM), 1991, vol. 33, issue 1, 23-31

Abstract: We apply a new interactive simulation environment for neural-network development to the development of mapping networks, which produce learned or preset functions of real inputs. Function-mapping networks are useful for adaptive control and as general-purpose, self-learning function generators. DESIRE/NEUNET describes neural networks in a reasonable matrix language. A built-in, extra-fast compiler lets screen-edited programs execute immediately, without annoying translation delays, and simulations run faster than Microsoft FORTRAN.

Date: 1991
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:33:y:1991:i:1:p:23-31

DOI: 10.1016/0378-4754(91)90021-T

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