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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Lin, Cheng-JianSubject: search.filters.subject.classification

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Now showing 1 - 4 of 4
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    A Hybrid of Bacterial Foraging Optimization and Particle Swarm Optimization for Evolutionary Neural Fuzzy Classifier
    (2014-09-01)
    Chen, Cheng-Hung
    ;
    Su, Miin-Tsair
    ;
    Lin, Cheng-Jian
    ;
    Lin, Chin-Teng
    ;
    交大名義發表
    This study presents a new evolutionary learning algorithm to optimize the parameters of the neural fuzzy classifier (NFC). This new evolutionary learning algorithm is based on a hybrid of bacterial foraging optimization and particle swarm optimization. It is thus called bacterial foraging particle swarm optimization (BFPSO). The proposed BFPSO method performs local search through the chemotactic movement operation of bacterial foraging whereas the global search over the entire search space is accomplished by a particle swarm operator. The NFC model uses functional link neural networks as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. Finally, the proposed neural fuzzy classifier with bacterial foraging particle swarm optimization (NFC-BFPSO) is adopted in several classification applications. Experimental results have demonstrated that the proposed NFC-BFPSO method can outperform other methods.
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    An entropy-based quantum neuro-fuzzy inference system for classification applications
    (2007-08-01)
    Lin, Cheng-Jian
    ;
    Chung, I-Fang
    ;
    Chen, Cheng-Hung
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    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. (C) 2006 Elsevier B.V. All rights reserved.
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    Classification and medical diagnosis using wavelet-based fuzzy neural networks
    (2008-03-01)
    Lin, Cheng-Jian
    ;
    Chen, Chenghung
    ;
    Lee, Chiyung
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    In this paper, we propose a Wavelet-based Fuzzy Neural Network (WFNN) for classification and medical diagnosis. The proposed WFNN integrates the wavelet transform functions into a fuzzy system. We use non-orthogonal and compactly supported functions as the wavelet neural network (WNN) bases. The goal of the WFNN model combined with WNN is to improve the accuracy of function approximation. An on-line structure/parameter learning algorithm is used in the WFNN. Structure learning is based on the input partitions to determine the number of fuzzy rules and wavelet functions, and parameter learning is based on the supervised gradient descent method to adjust the shape of the membership functions and the connection weights of the wavelet neural networks. Computer simulations were conducted to test the performance and applicability of the proposed system.
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    An efficient quantum neuro-fuzzy classifier based on fuzzy entropy and compensatory operation
    (2008-04-01)
    Chen, Cheng-Hung
    ;
    Lin, Cheng-Jian
    ;
    Lin, Chin-Teng
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    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.