| 標題：RGA-based On-Line Tuning of BMF Fuzzy-Neural Networks for Adaptive Control of Uncertain Nonlinear Systems |
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| 學年 | 97 |
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| 學期 | 2 |
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| 出版（發表）日期 | 2009/06/01 |
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| 作品名稱 | RGA-based On-Line Tuning of BMF Fuzzy-Neural Networks for Adaptive Control of Uncertain Nonlinear Systems |
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| 作品名稱（其他語言） | |
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| 著者 | Y.G Leu; W.Y. Wang; I.H. Li, |
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| 單位 | |
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| 出版者 | |
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| 著錄名稱、卷期、頁數 | Neurocomputing 72(10-12), p.2636-2642 |
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| 摘要 | In this paper, an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear systems is proposed by using a reduced-form genetic algorithm (RGA). Both the control points of B-spline membership functions (BMFs) and the weighting factors of the adaptive fuzzy-neural controller are tuned on-line via the RGA approach. Each gene represents an adjustable parameter of the BMF fuzzy-neural network with real number components. For the purpose of on-line tuning these parameters and evaluating the stability of the closed-loop system, a special fitness function is included in the RGA approach. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the RIAFC. To illustrate the feasibility and applicability of the proposed method, two examples of nonlinear systems controlled by the RIAFC are demonstrated. |
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| 關鍵字 | B-spline membership function;Fuzzy-neural network;Reduced-form genetic algorithm;Adaptive fuzzy-neural control |
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| 語言 | 英文 |
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| ISSN | |
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| 期刊性質 | 國外 |
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| 收錄於 | SCI; |
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| 產學合作 | |
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| 通訊作者 | |
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| 審稿制度 | 是 |
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| 國別 | 荷蘭加勒比區 |
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| 公開徵稿 | |
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| 出版型式 | ,電子版 |
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| 相關連結 | |
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