Abstract
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.
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Duda PO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New York
Fei L, Shengmei Z, Baoyu Z (2000) Quantum neural network in speech recognition. Proc IEEE Int Conf Signal Process 6:1267–1270
Halgamuge S, Glesner M (1994) Neural networks in designing fuzzy systems for real world applications. Fuzzy Sets Syst 65:1–12
Kasabov N (1996) Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems. Fuzzy Sets Syst 82:135–149
Kretzschmar R, Bueler R, Karayiannis NB, Eggimann F (2000) Quantum neural networks versus conventional feedforward neural networks: an experimental study. Proc IEEE Int Conf Signal Process 1:328–337
Lee HM (1998) A neural network classifier with disjunctive fuzzy information. Neural Netw 11(6):1113–1125
Lee HM, Chen CM, Chen JM, Jou YL (2001) An efficient fuzzy classifier with feature selection based on fuzzy entropy. IEEE Trans Syst Man Cybern B 31:426–432
Leshno M, Lin VY, Pinkus A, Schocken S (1993) Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw 6(6):861–867
Lin CJ, Chen CH (2003) Nonlinear system control using compensatory neuro-fuzzy networks. IEICE Trans Fundam Electron Commun Comput Sci E86-A(9):2309–2316
Lin CJ, Ho WH (2003) A pseudo-Gaussian-based compensatory neural fuzzy system. IEEE International Conference on Fuzzy Systems, pp 214–220
Lin CJ, Chen CH, Lee CY (2004) A self-adaptive quantum radial basis function network for classification applications. IEEE International Joint Conference on Neural Networks, Budapest, pp 3263–3268, July 25–29
Lovel BC, Bradley AP (1996) The multiscale classifier. IEEE Trans Pattern Anal Mach Intell 18:124–137
Nauck D, Kruse R (1997) A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy Sets Syst 89:277–288
Ouyang CS, Lee SJ (1999) An improved learning algorithm for rule refinement in neuro-fuzzy modeling. Third International Conference Knowledge-Based Intelligent Information Engineering Systems, pp 238–241
Paul S, Kumar S (2002) Subsethood-product fuzzy neural inference system (SuPFuNIS). IEEE Trans Neural Netw 13(3):578–599
Purushothaman G, Karayiannis NB (1997) Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks. IEEE Trans Neural Netw 8(3):679–693
Russo M (1998) FuGeNeSys—a fuzzy genetic neural system for fuzzy modeling. IEEE Trans Fuzzy Syst 6:373–388
Seker H, Evans DE, Aydin N, Yazgan E (2001) Compensatory fuzzy neural networks-based intelligent detection of abnormal neonatal cerebral doppler ultrasound waveforms. IEEE Trans Inf Technol Biomed 5(3):187–194
Setiono R, Liu H (1997) Neural-network feature selector. IEEE Trans Neural Netw 8(3):654–662
Simpson PK (1992) Fuzzy min-max neural networks—Part I: Classification. IEEE Trans Neural Netw 3:776-786
Wang JS, George Lee CS (2002) Self-adaptive neuro-fuzzy inference systems for classification applications. IEEE Trans Fuzzy Syst 10(6):790–802
Wu TP, Chen SM (1999) A new method for constructing membership functions and fuzzy rules from training examples. IEEE Trans Syst Man Cybern B 29:25–40
Zhang YQ, Kandel A (1998) Compensatory neurofuzzy systems with fast learning algorithms. IEEE Trans Neural Netw 9(1):83–105
Zimmermann HJ, Zysno P (1980) Latent connective in human decision. Fuzzy Sets Syst 4:31–51
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Chen, CH., Lin, CJ. & Lin, CT. An efficient quantum neuro-fuzzy classifier based on fuzzy entropy and compensatory operation. Soft Comput 12, 567–583 (2008). https://doi.org/10.1007/s00500-007-0229-0
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DOI: https://doi.org/10.1007/s00500-007-0229-0