TY - JOUR

T1 - An entropy-based quantum neuro-fuzzy inference system for classification applications

AU - Lin, Cheng Jian

AU - Chung, I. Fang

AU - Chen, Cheng Hung

N1 - Funding Information:
This work was supported by National Science Council, R.O.C., under Grant no. NSC94-2218-E-324-004.

PY - 2007/8

Y1 - 2007/8

N2 - 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.

AB - 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.

KW - Classification

KW - Entropy-based fuzzy model

KW - Neural fuzzy network

KW - Quantum function

KW - Self-clustering method

UR - http://www.scopus.com/inward/record.url?scp=34249697035&partnerID=8YFLogxK

U2 - 10.1016/j.neucom.2006.08.008

DO - 10.1016/j.neucom.2006.08.008

M3 - Article

AN - SCOPUS:34249697035

VL - 70

SP - 2502

EP - 2516

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

IS - 13-15

ER -