Abstract
A new fuzzy min-max neural network (FMNN) based on based on new algorithm is proposed for pattern classification. A new membership function of hyperbox is defined in which the characteristic are considered. The FMNN with new learning algorithm don’t use contraction process of fuzzy min-max neural network described by Simpson.The new algorithm only need expansion and no additional neurons have been added to the neural network to deal with the overlapped area. FMNN with new algorithm has strong robustness and high accuracy in classification for considering the characteristic of data core and noise. The performance of FMNN with new algorithm is checked by some benchmark data sets and compared with some traditional methods. All the results indicate that FMNN with new algorithm is effective. abstract environment.
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Ma, D., Liu, J., Wang, Z. (2012). The Pattern Classification Based on Fuzzy Min-max Neural Network with New Algorithm. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_1
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DOI: https://doi.org/10.1007/978-3-642-31362-2_1
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