Abstract
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 in the initial experiments reported here for a benchmark problem. Comparisons with results from the literature are also provided. To automatically segment the resultant neurons at the output, a toot from graph theory was used with promising results. General discussions and avenues for future works are also provided.