教師著作
Permanent URI for this collectionhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31268
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Item 線上基因演算之模糊類神經網路及其在非線性系統辨識與控制之應用(1/2)(行政院國家科學委員會, 2003-07-31) 王偉彥本計畫提出一種以基因演算為基礎輸出回授直接適應性模糊類神經控制器的設計法則，此控制器用以控制具未確定項之非線性動態系統。吾人使用一種reduced-form genetic algorithm (RGA)去調整模糊類神經控制器的權重因子，使得直接適應性模糊類神經控制器的權重因子可以基因演算方式線上調整。線上調整的適應函數是使用Lyapunov 設計方法推導。最後，加入監督式控制器確保控制系統的穩定性。Item Evolutionary learning of BMF fuzzy-neural networks using a reduced-form genetic algorithm(IEEE Systems, Man, and Cybernetics Society, 2003-12-01) W.-Y. Wang; Y.-H. LiIn this paper, a novel approach to adjust both the control points of B-spline membership functions (BMFs) and the weightings of fuzzy-neural networks using a reduced-form genetic algorithm (RGA) is proposed. Fuzzy-neural networks are traditionally trained by using gradient-based methods, which may fall into local minimum during the learning process. To overcome the problems encountered by the conventional learning methods, genetic algorithms are adopted because of their capabilities of directed random search for global optimization. It is well known, however, that the searching speed of the conventional genetic algorithms is not desirable. Such conventional genetic algorithms are inherently incapable of dealing with a vast number (over 100) of adjustable parameters in the fuzzy-neural networks. In this paper, the RGA is proposed by using a sequential-search-based crossover point (SSCP) method in which a better crossover point is determined and only the gene at the specified crossover point is crossed, serving as a single gene crossover operation. Chromosomes consisting of both, the control points of BMFs and the weightings of the fuzzy-neural network are coded as an adjustable vector with real number components that are searched by the RGA. Simulation results have shown that faster convergence of the evolution process searching for an optimal fuzzy-neural network can be achieved. Examples of nonlinear functions approximated by using the fuzzy-neural network via the RGA are demonstrated to illustrate the effectiveness of the proposed method.Item 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. LeuIn 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.