A comparative study of three neural networks that use soft competition

K. Butchart

    Research output: Book/ReportOther report

    112 Downloads (Pure)

    Abstract

    This report provides a comparative study of three proposed self-organising neural network models that use forms of soft competition. The use of soft competition helps the neural networks to avoid poor local minima and so provide a better interpretation of the data they are representing. The networks are also thought to be generally insensitive to initialisation conditions. The networks studied are the Deterministic Soft Competition Network (DSCN) of Yair et al., the Neural Gas network of Martinetz et al and the Generalised Learning Vector Quantisation (GLVQ) of Pal et al. The performance of the networks is compared to that of standard competitive networks and a Self Organising Map when run over a variety of data sets. The three proposed neural network models appear to produce enhanced results, particularly the Neural Gas network, but in case of the Neural Gas network and the DSCN this is at the cost of greater computational complexity.
    Original languageEnglish
    PublisherUniversity of Hertfordshire
    Publication statusPublished - 1994

    Publication series

    NameUH Computer Science Technical Report
    PublisherUniversity of Hertfordshire
    Volume211

    Fingerprint

    Dive into the research topics of 'A comparative study of three neural networks that use soft competition'. Together they form a unique fingerprint.

    Cite this