Exploration of large and high-dimensional data sets is one of the main problems in data analysis. Self-organizing feature maps (SOFM) is a powerful technique for clustering analysis and data mining. Competitive learning in the SOFM training process focuses on finding a neuron that its weight vector is most similar to that of an input vector. SOFM can be used to map large data sets to a simpler, usually one or two-dimensional topological structure. In this paper, we present a new approach to acquisition of initial fuzzy rules using SOFM learning algorithm, not only for its vector feature, but also for its topological. In general, fuzzy modeling requires two stages: structure identification and parameter learning. First, the algorithm partitions the input space into some local regions by using SOFM, then it determines the decision boundaries for local input regions, and finally, based on the decision boundaries, it learns the fuzzy rule for each local region by recursive least squares algorithm. The simulation results show that the proposed method can provide good model structure for fuzzy modeling and has high computing efficiency.
Digital Object Identifier (DOI)
Chen, Ching-Yi; Chiang, Jen-Shiun; Chen, Kuang-Yuan; Liu, Ta-Kang; and Wong, Ching-Chang
"An Approach for Fuzzy Modeling based on Self-Organizing Feature Maps Neural Network,"
Applied Mathematics & Information Sciences: Vol. 08:
3, Article 34.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol08/iss3/34