A set of scaling feedforward filters is developed in an unsupervised way via inputting pixel-discretized extended objects into a winner-take-all artificial neural network. The system discretizes the input space by both position and size. Depending on the distribution of input samples and below a certain number of neurons the spatial filters may form groups of similar filter sizes with each group covering the whole input space in a quasiuniform fashion. Thus a multi-discretizing system may be formed. Interneural connections of scaling filters are also developed with the help of extended objects. It is shown both theoretically and with the help of numerical simulation that competitive Hebbian learning is suitable for defining neighbours for the multi-discretizing system. Taking into account the neighbouring connections between filters of similar sizes only, i.e. within the groups of filters, the system may be considered as a self-organizing multi-grid system.
ASJC Scopus subject areas
- Computer Science(all)