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

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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.

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Authors and Affiliations

  1. Electronic Engineering Laboratory, University of Kent at Canterbury, CT2 7NT, Canterbury, Kent, UK

    Anne Canuto

  2. Electronic Engineering Laboratory, University of Kent at Canterbury, CT2 7NT, Canterbury, Kent, UK

    Gareth Howells

  3. Electronic Engineering Laboratory, University of Kent at Canterbury, CT2 7NT, Canterbury, Kent, UK

    Michael Fairhurst

Authors
  1. Anne Canuto
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  2. Gareth Howells
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  3. Michael Fairhurst
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Canuto, A., Howells, G. & Fairhurst, M. An Investigation of the Effects of Variable Vigilance within the RePART Neuro-Fuzzy Network. Journal of Intelligent and Robotic Systems 29, 317–334 (2000). https://doi.org/10.1023/A:1008159908688

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