A COMPARISON STUDY OF BINARY FEEDFORWARD NEURAL NETWORKS AND DIGITAL CIRCUITS

HMA ANDREE*, GT BARKEMA, W LOURENS, A TAAL, JC VERMEULEN, G.T. Barkema

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

A comparison study was carried out between feedforward neural networks composed of binary linear threshold units and digital circuits. These networks were generated by the regular partitioning algorithm and a modified Quine-McCluskey algorithm, respectively. The size of both types of networks and their generalisation properties are compared as a function of the nearest-neighbour correlation in the binary input sets. The ratio of the number of components required by digital circuits and the number of neurons grows linearly for the input sets considered The considered neural networks do not outperform digital circuits with respect to generalisation. Sensitivity analysis leads to a preference for digital circuits, especially for increasing number of inputs. In the case of analog input sets, hybrid networks of binary neurons and logic gates are of interest.

Original languageEnglish
Pages (from-to)785-790
Number of pages6
JournalNeural Networks
Volume6
Issue number6
Publication statusPublished - 1993

Keywords

  • BINARY FEEDFORWARD NEURAL NETWORKS
  • LOGIC CIRCUITS
  • HARDWIRED IMPLEMENTATION
  • ALGORITHM

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