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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SINGH H, ABDULLAH M Z, QUTIESHAT A. Detection and classification of electrical supply voltage quality to electrical motors using the fuzzy-min-max neural network [C]//IEEE International Electric Machines & Drives Conference (IEMDC). Niagara Falls: IEEE, 2011: 961–965.
GOSWAMI B, BHANDARI G, GOSWAMI S. Fuzzy min-max neural network for satellite infrared image clustering [C]//Third International Conference on Emerging Applications of Information Technology (EAIT). Kolkata: IEEE, 2012: 239–242.
KOTHARI R, JAIN V. Learning from labeled and unlabeled data using minimal number of queries [J]. IEEE Trans on Neural Networks, 2003, 14(6): 1096–1105.
SIMPSON P K. Fuzzy min-max neural networks [C]//IEEE International Joint Conference on Neural Networks. [s.l.]: IEEE, 1991: 1658–1669.
SIMPSON P K. Fuzzy min-max neural networks. Part 1. Classification [J]. IEEE Transactions on Neural Networks, 1992, 3(5): 766–786.
SIMPSON P K. Fuzzy min-max neural networks. Part 2. Clustering [J]. IEEE Transactions on Fuzzy Systems, 1993, 1(1): 32–45.
SHINDE S V, KULKARNI U V. Mining classification rules from fuzzy min-max neural network [C]//2014 International Conference on Computing, Communication and Networking Technologies (ICCCNT). Hefei: IEEE, 2014: 1–7.
DAVTALAB R, DEZFOULIAN M H, MANSOORIZADEH M. Multi-level fuzzy min-max neural network classifier [J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 3(25): 470–482.
GOSWAMI B, BHANDARI G, GOSWAMI S. Fuzzy min-max neural network for satellite infrared image clustering [C]//2012 Third International Conference on Emerging Applications of Information Technology (EAIT). Kolkata: IEEE, 2012: 239–242.
NANDEDKAR A V, BISWAS P K. A granular reflex fuzzy min-max neural network for classification [J]. IEEE Transactions on Neural Networks, 2009, 7(20): 1117–1134.
CHEN X, JIN D M, LI Z J. Recursive training for multi-resolution fuzzy min-max neural network classifier [C]//Proceedings of 6th International Conference on Solid-State and Integrated-Circuit Technology. Shanghai: IEEE, 2001: 131–134.
LIU J H, FENG J. Diagnosis for oil pipeline based on fuzzy min-max neural network [J]. Journal of Nanjing University of Aeronautics & Astronautics, 2011, 43(5): 199–202 (in Chinese).
HATTORI K, TAKAHASHI M. A new edited knearest neighbor rule in the pattern classification problem [J]. Pattern Recognition, 2000, 33(3): 521–528.
JOHNSON S C. Hierarchical clustering schemes [J]. Psychometrika, 1967, 32(3): 241–254.
BLAKE C, KEOGH E, MERZ C J. UCI repository of machine learning databases [EB/OL]. (2016-10-26). http://www.ics.uci.edu/∼mlearn/MLRepository.htm1. 1998.
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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