The influence of ART1 parameters setting on the behavior of layer 2

Zhong, Cheng; Cai, Zixing; Li, Ze-Wen; Wang, Xiaohua

doi:10.1109/icmlc.2005.1527788

Cited by 2 publications

(1 citation statement)

References 6 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Isawa et al (2009) the authors propose using variable vigilance parameters, where vigilance parameters are arranged for every category and varied according to the size of respective categories with learning and claimed more flexibility in classifying input data compared to the conventional Fuzzy ART. Chen et al (2005), the author carried out a simulation case to analyze the ART1 architecture and the membrane equation of layer 2 to describe the oscillation possibility of the activities of the neurons and studied the influence of parameters setting on the behavior of L2 layer. (2)…”

Section: Related Work: Al-natsheh and Eldosmentioning

confidence: 99%

Adaptive Resonance Theory Training Parameters: Pretty Good Sets

Eldos¹

2010

Journal of Computer Science

View full text Add to dashboard Cite

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.

show abstract

Section: Related Work: Al-natsheh and Eldosmentioning

confidence: 99%