کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
410570 679149 2009 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
RGA-based on-line tuning of BMF fuzzy-neural networks for adaptive control of uncertain nonlinear systems
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
RGA-based on-line tuning of BMF fuzzy-neural networks for adaptive control of uncertain nonlinear systems
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 72, Issues 10–12, June 2009, Pages 2636–2642
نویسندگان
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