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Supercircle neural network model based on fuzzy pattern recognition

2013-09-16
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Fuzzy pattern recognition is an important direction in the study of fuzzy set theory, and neural network is a common method in data mining. The learning time and network model understanding of the supercircular neural network are better than those of the BP neural network, and it can contain the same amount of information with less data. Based on the idea of the supercircular neural network model, this paper proposes a new neural network model algorithm based on fuzzy pattern recognition. The algorithm inherits the advantages of the supercircular neural network and can effectively learn samples. Keywords: fuzzy sets；data mining；fuzzy pattern recognition；super circle covering artificialneural networkResearch of the Algorithm of Artificial Neural Network based on Fuzzy Pattern RecognitionLiu Bing-xiang，Li Hai-lin，Li Hui-ying (School of Information Engineering, JDZ Ceramic Institute, JingDeZhen, 333001) Abstract: Fuzzy Pattern Recognition is an important research field on the fuzzy theory, Artificial neural network (ANN) is a common method of data mining. The learning period and network model understand of the super circle covering ANN are better than BP neural network. According to its thought, the new Artificial Neural Network based on fuzzy pattern recognition will be introduced. Comparing with the super circle covering ANN, this algorithm has the same excellent aspect, in practice it is useful to learn the samples.Key words: fuzzy sets；data mining；fuzzy pattern recognition；super circle covering artificialneural network

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