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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Lin, Cheng-JianHas files: Yes

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Now showing 1 - 10 of 12
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    A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks
    (2008)
    Chen, Cheng-Hung
    ;
    Liu, Yong-Cheng
    ;
    Lin, Cheng-Jian
    ;
    Lin, Chin-Teng
    ;
    電控工程研究所
    This study presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural networks as the consequent part of the fuzzy rules. Finally, the proposed functional-link-based neural fuzzy network with cultural cooperative particle swarm optimization (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems.
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    A Rule-Based Symbiotic MOdified Differential Evolution for Self-Organizing Neuro-Fuzzy Systems
    (2011-12-01)
    Su, Miin-Tsair
    ;
    Chen, Cheng-Hung
    ;
    Lin, Cheng-Jian
    ;
    Lin, Chin-Teng
    ;
    電機工程學系
    This study proposes a Rule-Based Symbiotic MOdified Differential Evolution (RSMODE) for Self-Organizing Neuro-Fuzzy Systems (SONFS). The RSMODE adopts a multi-subpopulation scheme that uses each individual represents a single fuzzy rule and each individual in each subpopulation evolves separately. The proposed RSMODE learning algorithm consists of structure learning and parameter learning for the SONFS model. The structure learning can determine whether or not to generate a new rule-based subpopulation which satisfies the fuzzy partition of input variables using the entropy measure. The parameter learning combines two strategies including a subpopulation symbiotic evolution and a modified differential evolution. The RSMODE can automatically generate initial subpopulation and each individual in each subpopulation evolves separately using a modified differential evolution. Finally, the proposed method is applied in various simulations. Results of this study demonstrate the effectiveness of the proposed RSMODE learning algorithm. (C) 2011 Elsevier B. V. All rights reserved.
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    A recurrent functional-link-based neural fuzzy system and its applications
    (2007)
    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 recurrent functional-link-based neural fuzzy system (RFLNFS) is proposed for prediction of time sequence and skin color detection. The proposed RFLNFS model uses functional link neural network as the consequent part of fuzzy rules. The RFLNFS model can generate the consequent part of a nonlinear combination of the input variables. The recurrent network is embedded in the RFLNFS by adding feedback connections in the second layer, where the feedback units act as memory elements. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. Finally, the RFLNFS is applied to two simulations. The simulation results of the dynamic system modeling have shown that the RFLNFS model can solve the temporal problem and the RFLNFS model has superior performance than other models.
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    Reinforcement hybrid evolutionary learning for recurrent wavelet-based neurofuzzy systems
    (2007-08-01)
    Lin, Cheng-Jian
    ;
    Hsu, Yung-Chi
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    This paper proposes a recurrent wavelet-based neurofuzzy system (RWNFS) with the reinforcement hybrid evolutionary learning algorithm (R-HELA) for solving various control problems. The proposed R-HELA combines the compact genetic algorithm (CGA), and the modified variable-length genetic algorithm (MVGA) performs the structure/parameter learning for dynamically constructing the RWNFS. That is, both the number of rules and the adjustment of parameters in the RWNFS are designed concurrently by the R-HELA. In the R-HELA, individuals of the same length constitute the same group. There are multiple groups in a population. The evolution of a population consists of three major operations: group reproduction using the compact genetic algorithm, variable two-part crossover, and variable two-part mutation. Illustrative examples were conducted to show the performance and applicability of the proposed R-HELA method.
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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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    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.
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    Efficient immune-based particle swarm optimization learning for neuro-fuzzy networks design
    (2008-09-01)
    Lin, Cheng-Jian
    ;
    Chen, Cheng-Hung
    ;
    Lee, Chi-Yung
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    In order to enhance the immune algorithm (IA) performance and find the optimal solution when dealing with difficult problems, we propose an efficient immune-based particle swarm optimization (IPSO) for use in TSK-type neuro-fuzzy networks for solving the identification and prediction problems. The proposed IPSO combines the immune algorithm (IA) and particle swarm optimization (PSO) to perform parameter learning. The IA uses the clonal selection principle, such that antibodies between others of high similar degree are affected, and these antibodies, after the process, will have higher quality, accelerating the search and increasing the global search capacity. The PSO algorithm has proved to be very effective for solving global optimization. It is not only a recently invented high-performance optimizer that is easy to understand and implement, but it also requires little computational bookkeeping and generally only a few lines of code. Hence, we employed the advantages of PSO to improve the mutation mechanism of immune algorithm. Experiments with synthetic and real data sets have performed in order to show the applicability of the proposed approach and also to compare with other methods in the literature.
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    A Functional-Link-Based Neurofuzzy Network for Nonlinear System Control
    (2008-10-01)
    Chen, Cheng-Hung
    ;
    Lin, Cheng-Jian
    ;
    Lin, Chin-Teng
    ;
    資訊工程學系
    ;
    電控工程研究所
    This study presents a functional-link-based neurofuzzy network (FLNFN) structure for nonlinear system control. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed FLNFN model is a nonlinear combination of input variables. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN. Furthermore, results for the universal approximator and a convergence analysis of the FLNFN model are proven. Finally, the FLNFN model is applied in various simulations. Results of this study demonstrate the effectiveness of the proposed FLNFN model.
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    Efficient Self-Evolving Evolutionary Learning for Neurofuzzy Inference Systems
    (2008-12-01)
    Lin, Cheng-Jian
    ;
    Chen, Cheng-Hung
    ;
    Lin, Chin-Teng
    ;
    資訊工程學系
    ;
    電控工程研究所
    This study proposes an efficient self-evolving evolutionary learning algorithm (SEELA) for neurofuzzy inference systems (NFISs). The major feature of the proposed SEELA is that it is based on evolutionary algorithms that can determine the number of fuzzy rules and adjust-the NFIS parameters. The SEELA consists of structure learning and parameter learning. The structure learning attempts to determine the number of fuzzy rules. A subgroup symbiotic evolution is adopted to yield several variable fuzzy systems, and an elite-based structure strategy is adopted to find a suitable number of fuzzy rules for solving a problem. The parameter learning is to adjust parameters of the NFIS. It is a hybrid evolutionary algorithm of cooperative particle swarm optimization (CPSO) and cultural algorithm, called cultural CPSO (CCPSO). The CCPSO, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Experimental results demonstrate that the proposed method performs well in predicting time series and solving nonlinear control problems.
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    A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications
    (2009-01-01)
    Lin, Cheng-Jian
    ;
    Chen, Cheng-Hung
    ;
    Lin, Chin-Teng
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    This study presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN 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. The FLNFN model can generate the consequent part of a nonlinear combination of input variables. Finally, the proposed FLNFN with CCPSO (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems.