Art-Based Autonomous Learning Systems: Part I — Architectures and Algorithms

  • C. P. Lim
  • R. F. Harrison
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 43)


This chapter describes the design of novel ART-based intelligent systems that are able to learn and, at the same time, to refine their knowledge in perpetuity. Fuzzy ARTMAP and the Probabilistic Neural Network are integrated to form a hybrid system that possesses the desirable properties for incremental, causal learning as well as for Bayesian probability estimation. Subsequently, a multiple neural network architecture is devised to aggregate outputs from several individual networks into a unified decision. A number of algorithms is proposed to increase the generalization and adaptability of the resulting systems.


Input Pattern Probabilistic Neural Network Target Class Belief Function Adaptive Resonance Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • C. P. Lim
    • 1
  • R. F. Harrison
    • 2
  1. 1.School of Industrial TechnologyUniversiti Sains MalaysiaPenangMalaysia
  2. 2.Department of Automatic Control and Systems EngineeringThe University of SheffieldSheffieldUK

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