An efficient quantum neuro-fuzzy classifier based on fuzzy entropy and compensatory operation


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… (More)
DOI: 10.1007/s00500-007-0229-0


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