Abstract
An integrated fuzzy min-max neural network (IFMMNN) is developed to avoid the classification result influenced by the input sequence of training samples, and the learning algorithm can be used as pure clustering, pure classification, or a hybrid clustering classification. Three experiments are designed to realize the aim. The serial input of samples is changed to parallel input, and the fuzzy membership function is substituted by similarity matrix. The experimental results show its superiority in contrast with the original method proposed by Simpson.
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Foundation item: the National Natural Science Foundation of China (No. 61402280)
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Hu, J., Luo, Y. Integration of learning algorithm on fuzzy min-max neural networks. J. Shanghai Jiaotong Univ. (Sci.) 22, 733–741 (2017). https://doi.org/10.1007/s12204-017-1894-5
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DOI: https://doi.org/10.1007/s12204-017-1894-5
Key words
- fuzzy min-max neural network (FMMNN)
- supervised and unsupervised learning
- clustering and classification
- learning algorithm
- similarity