Advances in Neuro-Information Processing
Visualizing high-dimensional input data with growing self-organizing maps
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
DNA13'07 Proceedings of the 13th international conference on DNA computing
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The different viable architectures that implement P-systems (membrane systems) over distributed clusters of processors have a major drawback: the distribution of these architectures in a balanced tree of processors can minimize external communications and maximize the parallelism grade. For a given P-system and K processors, there exists a great number of possible distributions of membranes. In a recent article, the feasibility of using self-organizing neural networks (SONN) with a growing capacity to help in the selection process of a distribution for a given P-system has been demonstrated, although the nature of the two-dimensional patterns used in the study limited the possibility of defining more flexible degrees of communication, making it more difficult to locate the best distribution. In this article, the capacity of a growing cell structure (GCS) model for projecting high-dimensional spaces in bi-dimensional graphs is explored.