Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Recent Developments In Biologically Inspired Computing
Recent Developments In Biologically Inspired Computing
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Immune K-means and negative selection algorithms for data analysis
Information Sciences: an International Journal
Adaptive radius immune algorithm for data clustering
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Biological plausibility in optimisation: an ecosystemic view
International Journal of Bio-Inspired Computation
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This paper proposes a novel constructive learning algorithm for a competitive neural network. The proposed algorithm is developed by taking ideas from the immune system and demonstrates robustness for data clustering in the initial experiments reported here for three benchmark problems. Comparisons with results from the literature are also provided. To automatically segment the resultant neurons at the output, a tool from graph theory was used with promising results. A brief sensitivity analysis of the algorithm was performed in order to investigate the influence of the main user-defined parameters on the learning speed and accuracy of the results presented. General discussions and avenues for future works are also provided.