A comparison of self-organizing feature map clustering with TWINSPAN and fuzzy C-means clustering in the analysis of woodland communities in the Guancen Mts, China

An Index for Measuring Functional Diversity in Plant Communities Based on Neural Network Theory

“…While SOFM index was measured in neural network topological space of functional traits, it is theoretically different from other indices, it can deal with much imprecise and incomplete fuzzy information, and it has advantages in solving non-linear problems and in studying complicated systems [24]. Theoretically, SOFM can describe natural phenomena and rules better [2,17]. The network can distribute work parallel and hence can calculate very quickly.…”

“…The Kohonen Self-Organizing Feature Map (SOFM) is one of the most well-known neural networks with unsupervised learning rules [11,[14][15][16], and it performs a topology-preserving projection of the data space onto a regular two-dimensional space. It has been successfully used in classification and ordination of plant communities [16,17]. Because of its theoretical advantages, it is expected to apply Self-Organizing Feature Map to functional diversity analysis.…”

Fuzzy Index and Self-Organizing Feature Map Index for Measuring Functional Diversity Applied to Acanthopanax Senticosus Communities

“…Fuzzy index is based on fuzzy mathematical theory and SOFM index is based on neural network theory, they are proved useful and have some advantages in plenty of applications including in plant ecology [19]. Theoretically, they should be good methods in evaluation of functional diversity in plant communities [8,20].…”