Innovations in ART Neural Networks

1. Front Matter

   Pages i-xii

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2. Novel Extension of ART2 in Surface Landmine Detection

   • A. Filippidis, M. Russo, L. C. Jain

   Pages 1-16

3. An Efficient Mapping of Fuzzy Art Onto a Neural Architecture

   • M. Blume

   Pages 17-25

4. Fuzzy Artmap Modifications for Intersecting Class Distributions

   • M. Blume, D. A. Van Blerkom

   Pages 27-54

5. Supervised Adaptive Resonance Theory and Rules

   • A.-H. Tan

   Pages 55-86

6. Incremental Learning and Optimization of Hierarchical Clusterings with Art-Based Modular Networks

   • G. Bartfai, R. White

   Pages 87-131

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

   • C. P. Lim, R. F. Harrison

   Pages 133-166

8. Art-Based Autonomous Learning Systems: Part II — Applications

   • C. P. Lim, R. F. Harrison

   Pages 167-188

9. Adaptive Resonance Theory Microchips

   • T. Serrano-Gotarredona, B. Linares-Barranco

   Pages 189-253

10. Back Matter

    Pages 255-259

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In the last two decades the artificial neural networks have been refined and widely used by the researchers and application engineers. We have not witnessed such a large degree of evolution in any other artificial neural network as in the Adaptive Resonance Theory (ART) neural network. The ART network remains plastic, or adaptive, in response to significant events and yet remains stable in response to irrelevant events. This stability-plasticity property is a great step towards realizing intelligent machines capable of autonomous learning in real time environment.
The main aim of this book is to report a very small sample of the research on the evolution of ART neural network and its applications. Interested readers may refer literature for many more innovations in ART such as Fuzzy ART, ART2, ART2-a, ARTMAP, ARTMAP-PI, ARTMAP-DS, Gaussian ARTMAP, EXACT ART, and ART-EMAP.

• Adaptive Resonance Theory
• Extension
• Fuzzy Logik
• Mapping
• algorithms
• artificial neural network
• evolution
• fuzzy logic
• innovation
• learning
• learning systems
• networks
• neural networks
• optimization
• time
• complexity

• Knowledge-Based Intelligent Engineering Systems Centre, University of South Australia, Adelaide, Mawson Lakes, South Australia

  Lakhmi C. Jain

• Dipartimento di Ingegneria della Informazione, University of Pisa, Pisa, Italy

  Beatrice Lazzerini

• Department of Electrical Engineering, Middle East Technical University, Ankara, Turkey

  Ugur Halici

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