標題: An efficient quantum neuro-fuzzy classifier based on fuzzy entropy and compensatory operation
作者: Chen, Cheng-Hung
Lin, Cheng-Jian
Lin, Chin-Teng
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: classification;compensatory operation;quantum function;self-clustering method;quantum fuzzy entropy;neuro-fuzzy network
公開日期: 1-Apr-2008
摘要: In this paper, a quantum neuro-fuzzy classifier (QNFC) for classification applications is proposed. The proposed QNFC model is a five-layer structure, which combines the compensatory-based fuzzy reasoning method with the traditional Takagi-Sugeno-Kang (TSK) fuzzy model. The compensatory-based fuzzy reasoning method uses adaptive fuzzy operations of neuro-fuzzy systems that can make the fuzzy logic system more adaptive and effective. Layer 2 of the QNFC model contains quantum membership functions, which are multilevel activation functions. Each quantum membership function is composed of the sum of sigmoid functions shifted by quantum intervals. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA), quantum fuzzy entropy and the backpropagation algorithm, is also proposed. The proposed SCA method is a fast, one-pass algorithm that dynamically estimates the number of clusters in an input data space. Quantum fuzzy entropy is employed to evaluate the information on pattern distribution in the pattern space. With this information, we can determine the number of quantum levels. The backpropagation algorithm is used to tune the adjustable parameters. The simulation results have shown that (1) the QNFC model converges quickly; (2) the QNFC model has a higher correct classification rate than other models.
URI: http://dx.doi.org/10.1007/s00500-007-0229-0
http://hdl.handle.net/11536/9525
ISSN: 1432-7643
DOI: 10.1007/s00500-007-0229-0
期刊: SOFT COMPUTING
Volume: 12
Issue: 6
起始頁: 567
結束頁: 583
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