教師著作

Permanent URI for this collectionhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31268

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Author: search.filters.author.W.-Y. WangSubject: search.filters.subject.genetic algorithms

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Now showing 1 - 3 of 3
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    A dynamic hierarchical fuzzy neural network for a general continuous function
    (2008-06-06) W.-Y. Wang; I-H. Li; S.-C. Li; M.-S. Tsai; S.-F. Su
    A serious problem limiting the applicability of the fuzzy neural networks is the "curse of dimensionality", especially for general continuous functions. A way to deal with this problem is to construct a dynamic hierarchical fuzzy neural network. In this paper, we propose a two-stage genetic algorithm to intelligently construct the dynamic hierarchical fuzzy neural network (HFNN) based on the merged-FNN for general continuous functions. First, we use a genetic algorithm which is popular for flowshop scheduling problems (GAFSP) to construct the HFNN. Then, a reduced-form genetic algorithm (RGA) optimizes the HFNN constructed by GAFSP. For a real-world application, the presented method is used to approximate the Taiwanese stock market.
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    An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems
    (IEEE Systems, Man, and Cybernetics Society, 2004-02-01) W.-Y. Wang; C.-Y. Cheng; Y.-G. Leu
    In this paper, we propose a novel design of a GA-based output-feedback direct adaptive fuzzy-neural controller (GODAF controller) for uncertain nonlinear dynamical systems. The weighting factors of the direct adaptive fuzzy-neural controller can successfully be tuned online via a GA approach. Because of the capability of genetic algorithms (GAs) in directed random search for global optimization, one is used to evolutionarily obtain the optimal weighting factors for the fuzzy-neural network. Specifically, we use a reduced-form genetic algorithm (RGA) to adjust the weightings of the fuzzy-neural network. In RGA, a sequential-search -based crossover point (SSCP) method determines a suitable crossover point before a single gene crossover actually takes place so that the speed of searching for an optimal weighting vector of the fuzzy-neural network can be improved. A new fitness function for online tuning the weighting vector of the fuzzy-neural controller is established by the Lyapunov design approach. A supervisory controller is incorporated into the GODAF controller to guarantee the stability of the closed-loop nonlinear system. Examples of nonlinear systems controlled by the GODAF controller are demonstrated to illustrate the effectiveness of the proposed method.
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    A Dynamic Hierarchical Fuzzy Neural Network for A General Continuous Function
    (中華民國模糊學會, 2009-06-01) W.-Y. Wang; I-H. Li; S.-C. Li; M.-S. Tsai; S.-F. Su
    A serious problem limiting the applicability of the fuzzy neural networks is the “curse of dimensionality”, especially for general continuous functions. A way to deal with this problem is to construct a dynamic hierarchical fuzzy neural network. In this paper, we propose a two-stage genetic algorithm to intelligently construct the dynamic hierarchical fuzzy neural network (HFNN) based on the merged-FNN for general continuous functions. First, we use a genetic algorithm which is popular for flowshop scheduling problems (GA_FSP) to construct the HFNN. Then, a reduced-form genetic algorithm (RGA) optimizes the HFNN constructed by GA_FSP. For a real-world application, the presented method is used to approximate the Taiwanese stock market.