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 -