教師資料查詢 | 類別: 期刊論文 | 教師: 許駿飛 Hsu, Chun-Fei (瀏覽個人網頁)

標題:Indirect adaptive self-organizing RBF neural controller design with a dynamical training approach
學年100
學期1
出版(發表)日期2012/01/01
作品名稱Indirect adaptive self-organizing RBF neural controller design with a dynamical training approach
作品名稱(其他語言)
著者Hsu, Chun-Fei; Chiu, Chien-Jung; Tsai, Jang-Zern
單位淡江大學電機工程學系
出版者Kidlington: Pergamon
著錄名稱、卷期、頁數Expert Systems with Applications 39(1), pp.564–573
摘要This study proposes an indirect adaptive self-organizing RBF neural control (IASRNC) system which is composed of a feedback controller, a neural identifier and a smooth compensator. The neural identifier which contains a self-organizing RBF (SORBF) network with structure and parameter learning is designed to online estimate a system dynamics using the gradient descent method. The SORBF network can add new hidden neurons and prune insignificant hidden neurons online. The smooth compensator is designed to dispel the effect of minimum approximation error introduced by the neural identifier in the Lyapunov stability theorem. In general, how to determine the learning rate of parameter adaptation laws usually requires some trial-and-error tuning procedures. This paper proposes a dynamical learning rate approach based on a discrete-type Lyapunov function to speed up the convergence of tracking error. Finally, the proposed IASRNC system is applied to control two chaotic systems. Simulation results verify that the proposed IASRNC scheme can achieve a favorable tracking performance.
關鍵字RBF network;Adaptive control;Neural control;Self-organizing;Dynamical learning rate
語言英文
ISSN1873-6793
期刊性質國外
收錄於SCI;
產學合作
通訊作者Hsu, Chun-Fei
審稿制度
國別英國
公開徵稿
出版型式,電子版,紙本
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