Uncertain Rule-based Fuzzy Neural Systems Development and Applications

Autor:	Yi-Han Lee, 李易翰
Druh dokumentu:	學位論文 ; thesis
Popis:	100 This paper proposes an uncertain rule-based fuzzy neural system using sinusoidal perturbation (UFNS-S) for identifying and controlling nonlinear system. The UFNS-S is proposed for simplifying the computational complexity of interval type-2 fuzzy neural network (IT2FNN) or interval type-2 fuzzy logic systems. The sinusoidal perturbations are adopted to combine with the fuzzy sets of antecedent and consequent part for UFNS-S, it is utilized to represent the footprint of uncertainty for interval type-2 fuzzy systems. Thus, the proposed UFNS-Ss reduce the computational complexity and have the ability of handling uncertainty. In addition, the back-propagation (BP) algorithm is adopted for training parameters of UFNS-S and to minimize the different between desired and UFNS-S’s outputs. Based on Lyapunov stability approach, the convergence of UFNS-S is guaranteed by choosing appropriate learning rates. In addition, the time-varying optimal learning rates are also derived to obtain the faster convergent speed. Finally, the effectiveness of the proposed approach is demonstrated by several examples that consist of computational complexity analysis, nonlinear system identification, and tracking control of two-link robot manipulator system.
Databáze:	Networked Digital Library of Theses & Dissertations
Externí odkaz:	http://ndltd.ncl.edu.tw/handle/53502842686559937382 Zobrazit plný text záznamu