Automatic generation of C++ code for neural network simulation

Stephan Dreiseitl, Dongming Wang

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

1 Citation (Scopus)


Coding neural network simulators by hand is often a tedious and error-prone task. In this paper, we seek to remedy this situation by presenting a code generator that produces efficient C++ simulation code for a wide variety of backpropagation networks. We define a high-level, Maple-like language that allows the specification of such networks. This language is compiled to C++ code segments that in turn are executable in link with an already given generic code for backpropagation networks. Our generator allows the specification of arbitrary network topologies (with the restriction of full connections between layers) and weightchange formulae, while the activation rule and error propagation rule remain fixed. With this tool, future research on learning rules for backpropagatiou networks can be made more efficient by eliminating routine work and producing code that is guaranteed to be error-free.

Original languageEnglish
Title of host publicationNew Trends in Neural Computation - International Workshop on Artificial Neural Networks, IWANN 1993, Proceedings
EditorsJose Mira, Joan Cabestany, Alberto Prieto
Number of pages6
ISBN (Print)9783540567981
Publication statusPublished - 1993
Externally publishedYes
EventInternational Workshop on Artificial Neural Networks, IWANN 1993 - Sitges, Spain
Duration: 9 Jun 199311 Jun 1993

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Workshop on Artificial Neural Networks, IWANN 1993


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