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

Cheng-Hung Chen, Cheng-Jian Lin, Chin-Teng Lin

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


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.
Original languageAmerican English
Pages (from-to)567-583
Number of pages17
JournalSoft Computing
Issue number6
StatePublished - Apr 2008


  • classification
  • compensatory operation
  • quantum function
  • self-clustering method
  • quantum fuzzy entropy
  • Neuro-fuzzy networks

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