TY  - JOUR
AU  - Eldos, Taisir Mohammad 
AU  - Almazyad, Abdulaziz Suleiman 
PY  - 2010
TI  - Adaptive Resonance Theory Training Parameters: Pretty Good Sets
JF  - Journal of Computer Science
VL  - 6
IS  - 12
DO  - 10.3844/jcssp.2010.1443.1449
UR  - https://thescipub.com/abstract/jcssp.2010.1443.1449
AB  - Problem statement: ART1 artificial neural networks offer good tools for test clustering,
where no expert is needed if the system is well trained. However, having no output reference for the input
patterns makes it hard to judge the quality of the training. Moreover, the performance depends to a great
extent on a set of training parameters. Designers follow some recommendations or depend on their
expertise in finding good sets with no performance guarantees. Many methods were proposed; from
greedy methods offering quick and acceptable solutions to evolutionary algorithms offering suboptimal
sets of parameters. While the evolutionary algorithms are a good choice for quality, the computational
cost is large even for an offline process; after all, computing resources are not for free. Approach: We
introduced a method for selecting a set of parameters that yields a comparable performance and robust
operation, with relatively low cost compared to the evolutionary methods. This method located a suitable
set through repetitive portioning of the range, by considering the best subset for the next iteration.
Results: Tests have shown that performance comparable with the computationally intensive evolutionary
methods could be achieved in much less time. Conclusion: The repetitive portioning method for finding
a good set of training parameters is very cost effective and yields good performance.