Cluster Computing

, Volume 22, Supplement 5, pp 11163–11174 | Cite as

Fuzzy c-means clustering algorithm for performance improvement of ENN

  • Yu ZhouEmail author
  • Qinchai Ren


In this work, with the purpose of improving the performance of extension neural network (ENN), we use Fuzzy c-means (FCM) clustering algorithm to locate the initial centers of every class before the training. In traditional ENN, the initial centers are defined simply by the average values of the minimum and maximum of every characteristic. Our proposed FENN (FCM–ENN) in this paper is different from tradition ENN, and the initial centers of every class are determined by the cluster centers of FCM clustering algorism. Our proposed strategy can reflect the actual training data distribution information, thereby the performance of ENN by using this strategy is more approach to practical situation. Compared with traditional ENN, the proposed FENN has a better performance. Experimental results from three different examples, including an artificial data set, a benchmark data set and a practical application, verify the effectiveness and applicability of our proposed work.


Fuzzy c-means (FCM) Extension neural network (ENN) Fuzzy extension neural network (FENN) Initial centers Cluster centers 



This work is supported by National Natural Science Foundation of China under Grant No. U1504622 and the Research Foundation of Education Bureau of Henan Province, China under Grant No. 14B120002. The authors would like to thank the anonymous reviewers and the editor for the very instructive suggestions that led to the much improved quality of this paper.


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© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Electric PowerNorth China University of Water Resources and Electric PowerZhengzhouChina

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