An entropy-based quantum neuro-fuzzy inference system for classification applications

Cheng Jian Lin*, I. Fang Chung, Cheng Hung Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

In this paper, an entropy-based quantum neuro-fuzzy inference system (EQNFIS) for classification applications is proposed. The EQNFIS model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK). Layer 2 of the EQNFIS 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. Simulations were conducted to show the performance and applicability of the proposed model.

Original languageEnglish
Pages (from-to)2502-2516
Number of pages15
JournalNeurocomputing
Volume70
Issue number13-15
DOIs
StatePublished - Aug 2007

Keywords

  • Classification
  • Entropy-based fuzzy model
  • Neural fuzzy network
  • Quantum function
  • Self-clustering method

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