This paper 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 the case of the Neural Gas network and the DSCN this is at the cost of greater computational complexity.
CITATION STYLE
Butchart, K., Davey, N., & Adams, R. (1995). A comparative study of three neural networks that use soft competition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 930, pp. 308–314). Springer Verlag. https://doi.org/10.1007/3-540-59497-3_190
Mendeley helps you to discover research relevant for your work.