Fuzzy-Kohonen-clustering neural network trained by genetic algorithm and fuzzy competition learning

Weixing Xie; Wenhua Li; Xinbo Gao

doi:10.1117/12.217539

28 August 1995 Fuzzy-Kohonen-clustering neural network trained by genetic algorithm and fuzzy competition learning

Weixing Xie, Wenhua Li, Xinbo Gao

Proceedings Volume 2620, International Conference on Intelligent Manufacturing; (1995) https://doi.org/10.1117/12.217539
Event: International Conference on Intelligent Manufacturing, 1995, Wuhan, China

Abstract

Kohonen networks are well known for clustering analysis. Classical Kohonen networks for hard c-means clustering (trained by winner-take-all learning) have some severe drawbacks. Fuzzy Kohonen networks (FKCNN) for fuzzy c-means clustering are trained by fuzzy competition learning, and can get better clustering results than the classical Kohonen networks. However, both winner-take-all and fuzzy competition learning algorithms are in essence local search techniques that search for the optimum by using a hill-climbing technique. Thus, they often fail in the search for the global optimum. In this paper we combine genetic algorithms (GAs) with fuzzy competition learning to train the FKCNN. Our experimental results show that the proposed GA/FC learning algorithm has much higher probabilities of finding the global optimal solutions than either the winner-take-all or the fuzzy competition learning.

Citation Download Citation

Weixing Xie, Wenhua Li, and Xinbo Gao "Fuzzy-Kohonen-clustering neural network trained by genetic algorithm and fuzzy competition learning", Proc. SPIE 2620, International Conference on Intelligent Manufacturing, (28 August 1995); https://doi.org/10.1117/12.217539

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