An Investigation of the Effects of Variable Vigilance within the RePART Neuro-Fuzzy Network

  • Authors:

  • Anne Canuto;Gareth Howells;Michael Fairhurst

  • Affiliations:

  • Electronic Engineering Laboratory, University of Kent at Canterbury, CT2 7NT, Canterbury, Kent, UK/ E-mail: amdc1@ukc.ac.uk;Electronic Engineering Laboratory, University of Kent at Canterbury, CT2 7NT, Canterbury, Kent, UK/ E-mail: w.g.j.howells@ukc.ac.uk;Electronic Engineering Laboratory, University of Kent at Canterbury, CT2 7NT, Canterbury, Kent, UK/ E-mail: m.c.fairhurst@ukc.ac.uk

  • Venue:
  • Journal of Intelligent and Robotic Systems
  • Year:
  • 2000

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Abstract

RePART is a variation of fuzzy ARTMAP to which a reward/punishment concept has been added. Previously, an improvement in performance of RePART had been noted compared with other ARTMAP-based models, such as fuzzy ARTMAP and ARTMAP-IC. In this paper, a wider investigation of RePART performance is described, in which RePART is analysed in relation to a multi-layer perceptron and a RAM-based network in a handwritten numeral recognition task. In the RePART network, a variable vigilance parameter is proposed in order to smooth the poor-generalisation problem of RePART. Firstly, the same vigilance is associated within every neuron – general variable vigilance. Secondly, an individual variable vigilance for each neuron – which takes into account its average and frequency of activation – is used. In a handwritten numeral recognition task using individual variable vigilance, RePART performance improved and demonstrated a performance comparable with alternative architectures such as fuzzy multi-layer perceptron and Radial RAM.