RGA-based On-Line Tuning of BMF Fuzzy-Neural Networks for Adaptive Control of Uncertain Nonlinear Systems
學年 97
學期 2
出版（發表）日期 2009-06-01
作品名稱 RGA-based On-Line Tuning of BMF Fuzzy-Neural Networks for Adaptive Control of Uncertain Nonlinear Systems
作品名稱（其他語言）
著者 Y.G Leu; W.Y. Wang; I.H. Li,
單位
出版者
著錄名稱、卷期、頁數 Neurocomputing 72(10-12), p.2636-2642
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.
關鍵字 B-spline membership function;Fuzzy-neural network;Reduced-form genetic algorithm;Adaptive fuzzy-neural control
語言 en
ISSN
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 是
國別 BES
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/115966 )

SDGS 產業創新與基礎設施